fix: Move trtllm multimodal worker to use ModelInput.Token (#4373)

Signed-off-by: Indrajit Bhosale <iamindrajitb@gmail.com>

components/src/dynamo/trtllm/encode_helper.py

@@ -202,7 +202,10 @@ class EncodeHelper:
             Response dictionary with NIXL metadata and embeddings info, or error response
         """
         # Load embeddings first to get the actual shape
-        messages = request.get("messages", [])
+        # Extract messages from extra_args (set by Rust preprocessor for multimodal) or fall back to direct field
+        messages = request.get("extra_args", {}).get(
+            "messages", request.get("messages", [])
+        )
         _, _, embedding_paths = multimodal_processor.extract_prompt_and_media(messages)
 
         if not embedding_paths:

components/src/dynamo/trtllm/main.py

@@ -261,7 +261,6 @@ async def init(runtime: DistributedRuntime, config: Config):
 
     if modality == "multimodal":
         engine_args["skip_tokenizer_init"] = False
-        model_input = ModelInput.Text
         model_config = AutoConfig.from_pretrained(
             config.model_path, trust_remote_code=True
         )

components/src/dynamo/trtllm/multimodal_processor.py

@@ -63,6 +63,9 @@ class MultimodalRequestProcessor:
     def is_url(self, path: str) -> bool:
         """Check if a path is a URL."""
         parsed = urlparse(path)
+        # file:// URLs have scheme but no netloc, treat them as local paths
+        if parsed.scheme == "file":
+            return False
         return bool(parsed.scheme and parsed.netloc)
 
     def load_tensor_from_path_or_url(self, path: str) -> torch.Tensor:
@@ -97,7 +100,10 @@ class MultimodalRequestProcessor:
                     )
                     raise RuntimeError("Failed to load tensor")
 
-                resolved_path = Path(path).resolve()
+                # Strip file:// prefix if present
+                local_path = path.removeprefix("file://")
+
+                resolved_path = Path(local_path).resolve()
                 allowed_path = Path(self.allowed_local_media_path).resolve()
 
                 # Secure path validation: Check if the resolved path is actually within allowed directory
@@ -151,21 +157,11 @@ class MultimodalRequestProcessor:
         self, request: Dict, embeddings: Any
     ) -> Optional[Any]:
         """Process OpenAI request and return with multimodal data."""
-        # Normalize the request to handle OpenAI format
-        if "stop_conditions" not in request:
-            request["stop_conditions"] = {}
-        if "max_tokens" in request and "max_tokens" not in request["stop_conditions"]:
-            request["stop_conditions"]["max_tokens"] = request.pop("max_tokens")
-
-        if "sampling_options" not in request:
-            request["sampling_options"] = {}
-        if (
-            "temperature" in request
-            and "temperature" not in request["sampling_options"]
-        ):
-            request["sampling_options"]["temperature"] = request.pop("temperature")
-
-        messages = request.get("messages", [])
+        # Extract messages - check extra_args first (from Rust preprocessor for multimodal)
+        # Fall back to direct messages field for backward compatibility
+        messages = request.get("extra_args", {}).get(
+            "messages", request.get("messages", [])
+        )
         text_prompt, image_urls, embedding_paths = self.extract_prompt_and_media(
             messages
         )
@@ -241,19 +237,3 @@ class MultimodalRequestProcessor:
             "object": "chat.completion.chunk",
             "choices": [choice],
         }
-
-    def get_stop_response(self, request_id: str, model_name: str) -> Dict[str, Any]:
-        """Creates the final stop response chunk for multimodal streaming."""
-        final_choice = {
-            "index": 0,
-            "delta": {},
-            "finish_reason": "stop",
-        }
-        return {
-            "id": request_id,
-            "model": model_name,
-            "created": int(time.time()),
-            "object": "chat.completion.chunk",
-            "choices": [final_choice],
-            "finish_reason": "stop",
-        }

components/src/dynamo/trtllm/request_handlers/handler_base.py

@@ -251,7 +251,6 @@ class HandlerBase:
         )
 
         request_id = request.get("id") or request.get("request_id", "unknown-id")
-        model_name = request.get("model", "unknown_model")
 
         # Optional test-only logits processing (enable with DYNAMO_ENABLE_TEST_LOGITS_PROCESSOR=1)
         if os.getenv("DYNAMO_ENABLE_TEST_LOGITS_PROCESSOR") == "1":
@@ -277,18 +276,6 @@ class HandlerBase:
                         self.publisher.start()
                         self.first_generation = False
 
-                    # Upon completion, send a final chunk with "stop" as the finish reason.
-                    # This signals to the client that the stream has ended.
-                    if (
-                        res.finished
-                        and self.disaggregation_mode != DisaggregationMode.PREFILL
-                    ):
-                        if self.multimodal_processor:
-                            final_out = self.multimodal_processor.get_stop_response(
-                                request_id, model_name
-                            )
-                            yield final_out
-
                     # If we are not done generating, but there are no outputs, return an error
                     if not res.outputs and not res.finished:
                         yield {"finish_reason": "error", "token_ids": []}
@@ -298,12 +285,9 @@ class HandlerBase:
                     # The engine returns all tokens generated so far. We must calculate the new
                     # tokens generated in this iteration to create the "delta".
                     next_total_toks = len(output.token_ids)
-                    if self.multimodal_processor:
-                        out = self.multimodal_processor.create_response_chunk(
-                            output, num_output_tokens_so_far, request_id, model_name
-                        )
-                    else:
+
                     out = {"token_ids": output.token_ids[num_output_tokens_so_far:]}
+
                     if output.finish_reason:
                         out["finish_reason"] = output.finish_reason
                     if output.stop_reason:

components/src/dynamo/trtllm/request_handlers/handlers.py

@@ -111,8 +111,12 @@ class PrefillHandler(HandlerBase):
         embeddings_tensor = None
 
         if self.multimodal_processor:
+            # Extract messages from extra_args (set by Rust preprocessor) or fall back to direct field
+            messages = request.get("extra_args", {}).get(
+                "messages", request.get("messages", [])
+            )
             _, _, embedding_paths = self.multimodal_processor.extract_prompt_and_media(
-                request.get("messages", [])
+                messages
             )
             if embedding_paths:
                 if self.encode_client and self.connector:

docs/backends/trtllm/multimodal_epd.md

@@ -23,10 +23,14 @@ To enable it build the dynamo container with the `--tensorrtllm-commit` flag, fo
 ```bash
 cd $DYNAMO_HOME/examples/backends/trtllm
 
-# Launch 3-worker EPD flow with NIXL
+# Launch 3-worker EPD flow with NIXL.
 ./launch/epd_disagg.sh
 ```
 
+## Pre-requsites
+
+This script is specifically designed to work on 8 node H200 and `Llama-4-Maverick-17B-128E-Instruct` model with assumption that you already have a model specific embedding file ready.
+
 ## Configuration
 
 The EPD flow uses a dedicated **Encode Worker** that runs separately from the Prefill and Decode workers. The `ENCODE_ENDPOINT` environment variable specifies how the Prefill worker communicates with the Encode worker:

examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/decode.yaml

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+tensor_parallel_size: 4
+moe_expert_parallel_size: 1
+enable_attention_dp: false
+max_num_tokens: 8192
+max_batch_size: 16
+trust_remote_code: true
+backend: pytorch
+enable_chunked_prefill: true
+disable_overlap_scheduler: false
+kv_cache_config:
+  free_gpu_memory_fraction: 0.30
+  enable_block_reuse: false
+
+cache_transceiver_config:
+  backend: DEFAULT
\ No newline at end of file

examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/encode.yaml

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+tensor_parallel_size: 1
+moe_expert_parallel_size: 1
+enable_attention_dp: false
+max_num_tokens: 8192
+max_batch_size: 16
+trust_remote_code: true
+backend: pytorch
+enable_chunked_prefill: true
+disable_overlap_scheduler: false
+kv_cache_config:
+  free_gpu_memory_fraction: 0.30
+  enable_block_reuse: false
+
+cache_transceiver_config:
+  backend: DEFAULT
\ No newline at end of file

examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/prefill.yaml

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+tensor_parallel_size: 4
+moe_expert_parallel_size: 1
+enable_attention_dp: false
+max_num_tokens: 8192
+max_batch_size: 16
+trust_remote_code: true
+backend: pytorch
+enable_chunked_prefill: true
+# Overlap scheduler not currently supported in prefill only workers.
+disable_overlap_scheduler: true
+
+kv_cache_config:
+  free_gpu_memory_fraction: 0.30
+  enable_block_reuse: false
+
+cache_transceiver_config:
+  backend: DEFAULT
\ No newline at end of file

examples/backends/trtllm/launch/epd_disagg.sh

@@ -4,14 +4,15 @@
 
 # Environment variables with defaults
 export DYNAMO_HOME=${DYNAMO_HOME:-"/workspace"}
-export MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2-VL-7B-Instruct"}
-export SERVED_MODEL_NAME=${SERVED_MODEL_NAME:-"Qwen/Qwen2-VL-7B-Instruct"}
-export PREFILL_ENGINE_ARGS=${PREFILL_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/qwen2-vl-7b-instruct/prefill.yaml"}
-export DECODE_ENGINE_ARGS=${DECODE_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/qwen2-vl-7b-instruct/decode.yaml"}
-export ENCODE_ENGINE_ARGS=${ENCODE_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/qwen2-vl-7b-instruct/encode.yaml"}
-export PREFILL_CUDA_VISIBLE_DEVICES=${PREFILL_CUDA_VISIBLE_DEVICES:-"0"}
-export DECODE_CUDA_VISIBLE_DEVICES=${DECODE_CUDA_VISIBLE_DEVICES:-"1"}
-export ENCODE_CUDA_VISIBLE_DEVICES=${ENCODE_CUDA_VISIBLE_DEVICES:-"2"}
+export MODEL_PATH=${MODEL_PATH:-"meta-llama/Llama-4-Scout-17B-16E-Instruct"}
+export SERVED_MODEL_NAME=${SERVED_MODEL_NAME:-"meta-llama/Llama-4-Scout-17B-16E-Instruct"}
+export PREFILL_ENGINE_ARGS=${PREFILL_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/prefill.yaml"}
+export DECODE_ENGINE_ARGS=${DECODE_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/decode.yaml"}
+# Placeholder for now, this is NO-OP as encoder just loads embeddings path, done to maintain consistency with other workers adn future api enhancements
+export ENCODE_ENGINE_ARGS=${ENCODE_ENGINE_ARGS:-"$DYNAMO_HOME/examples/backends/trtllm/engine_configs/llama4/multimodal/llama4-Scout/encode.yaml"}
+export PREFILL_CUDA_VISIBLE_DEVICES=${PREFILL_CUDA_VISIBLE_DEVICES:-"0,1,2,3"}
+export DECODE_CUDA_VISIBLE_DEVICES=${DECODE_CUDA_VISIBLE_DEVICES:-"4,5,6,7"}
+export ENCODE_CUDA_VISIBLE_DEVICES=${ENCODE_CUDA_VISIBLE_DEVICES:-"0"}
 export ENCODE_ENDPOINT=${ENCODE_ENDPOINT:-"dyn://dynamo.tensorrt_llm_encode.generate"}
 export MODALITY=${MODALITY:-"multimodal"}
 export ALLOWED_LOCAL_MEDIA_PATH=${ALLOWED_LOCAL_MEDIA_PATH:-"/tmp"}

lib/llm/src/preprocessor.rs

@@ -339,6 +339,14 @@ impl OpenAIPreprocessor {
 
         if !media_map.is_empty() {
             builder.multi_modal_data(Some(media_map));
+
+            // Preserve original messages in extra_args for multimodal workers that need them
+            // (e.g., TRT-LLM multimodal processor needs raw messages for proper tokenization)
+            let messages_json = serde_json::to_value(&messages)?;
+            let extra_args = serde_json::json!({
+                "messages": messages_json
+            });
+            builder.extra_args(Some(extra_args));
         }
 
         Ok(())