feat: propagate OTEL trace context across E/P/D multimodal workers (#7239)

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

@@ -21,6 +21,7 @@ from dynamo.common.multimodal.embedding_transfer import (
     NixlWriteEmbeddingReceiver,
 )
 from dynamo.common.utils import nvtx_utils as _nvtx
+from dynamo.common.utils.otel_tracing import build_trace_headers
 from dynamo.common.utils.time_section import time_and_log_code_section
 from dynamo.runtime import Client, DistributedRuntime
 
@@ -156,7 +157,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
     # ── Multimodal data loading ──────────────────────────────────────
 
     async def _load_multimodal_data(
-        self, image_urls: list[str], request_id: str
+        self, image_urls: list[str], request_id: str, context=None
     ) -> dict[str, Any]:
         """Fetch embeddings from encode workers and load into an engine-ready dict.
 
@@ -174,6 +175,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
             model=self.config.model,
             embeddings_dtype=self.EMBEDDINGS_DTYPE,
             cache=self.embedding_cache_manager,
+            context=context,
         )
 
     # ── Request metadata finalization ────────────────────────────────
@@ -260,9 +262,11 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
         request: vLLMMultimodalRequest,
         multi_modal_data: dict[str, Any],
         rng_ttft=None,
+        context=None,
     ):
         """Run prefill and decode on this worker (aggregated mode)."""
         lora_request = self._resolve_lora_request(request.model)
+        trace_headers = build_trace_headers(context) if context else None
         gen = self.engine_client.generate(
             prompt=TokensPrompt(
                 prompt_token_ids=request.engine_prompt["prompt_token_ids"],
@@ -271,6 +275,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
             sampling_params=request.sampling_params,
             request_id=request.request_id,
             lora_request=lora_request,
+            trace_headers=trace_headers,
         )
 
         num_output_tokens_so_far = 0
@@ -302,6 +307,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
         request: vLLMMultimodalRequest,
         multi_modal_data: dict[str, Any],
         rng_ttft=None,
+        context=None,
     ):
         """Prefill locally, then forward to a remote decode worker."""
         with _nvtx.annotate(
@@ -319,6 +325,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
             logger.debug("Prefill request: %s", prefill_only_request)
 
             lora_request = self._resolve_lora_request(request.model)
+            trace_headers = build_trace_headers(context) if context else None
             gen = self.engine_client.generate(
                 prompt=TokensPrompt(
                     prompt_token_ids=prefill_only_request.engine_prompt[
@@ -329,6 +336,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
                 sampling_params=prefill_only_request.sampling_params,
                 request_id=prefill_only_request.request_id,
                 lora_request=lora_request,
+                trace_headers=trace_headers,
             )
 
             # Drain prefill generator (max_tokens=1, expect a single response)
@@ -382,7 +390,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
             async for (
                 decode_response
             ) in await self.decode_worker_client.round_robin(  # type: ignore
-                request.model_dump_json()
+                request.model_dump_json(), context=context
             ):
                 output = MyRequestOutput.model_validate_json(decode_response.data())  # type: ignore
                 yield self._format_engine_output(output, num_output_tokens_so_far)
@@ -406,7 +414,7 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
 
             rng_load = _nvtx.start_range("mm:pd:load_multimodal", color="yellow")
             multi_modal_data = await self._load_multimodal_data(
-                image_urls, request.request_id
+                image_urls, request.request_id, context
             )
             _nvtx.end_range(rng_load)
 
@@ -415,13 +423,15 @@ class MultimodalPDWorkerHandler(BaseWorkerHandler):
         if self.enable_disagg and self.decode_worker_client:
             rng_disagg = _nvtx.start_range("mm:pd:generate_disagg", color="red")
             async for chunk in self._generate_disagg(
-                request, multi_modal_data, rng_ttft
+                request, multi_modal_data, rng_ttft, context=context
             ):
                 yield chunk
             _nvtx.end_range(rng_disagg)
         else:
             rng_agg = _nvtx.start_range("mm:pd:generate_agg", color="red")
-            async for chunk in self._generate_agg(request, multi_modal_data, rng_ttft):
+            async for chunk in self._generate_agg(
+                request, multi_modal_data, rng_ttft, context=context
+            ):
                 yield chunk
             _nvtx.end_range(rng_agg)
 

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

@@ -7,6 +7,7 @@ from vllm.inputs.data import TokensPrompt
 
 import dynamo.nixl_connect as connect
 from dynamo.common.utils import nvtx_utils as _nvtx
+from dynamo.common.utils.otel_tracing import build_trace_headers
 from dynamo.common.utils.time_section import time_and_log_code_section
 from dynamo.runtime import DistributedRuntime
 
@@ -57,14 +58,14 @@ class MultimodalDecodeWorkerHandler(BaseWorkerHandler):
     async def generate(self, request: vLLMMultimodalRequest, context):
         rng_decode = _nvtx.start_range("mm:decode_worker_generate", color="blue")
         logger.debug(f"Got raw request: {request}")
+        if not isinstance(request, vLLMMultimodalRequest):
+            if isinstance(request, str):
+                request = vLLMMultimodalRequest.model_validate_json(request)
+            else:
+                request = vLLMMultimodalRequest.model_validate(request)
         with time_and_log_code_section(
             f"[DECODE] request: {request.request_id} preprocessing time"
         ):
-            if not isinstance(request, vLLMMultimodalRequest):
-                if isinstance(request, str):
-                    request = vLLMMultimodalRequest.model_validate_json(request)
-                else:
-                    request = vLLMMultimodalRequest.model_validate(request)
             logger.debug(f"Received decode request: {{ id: {request.request_id} }}.")
 
             # For Qwen VL models with mRoPE, we need to pass multi_modal_data containing
@@ -90,6 +91,7 @@ class MultimodalDecodeWorkerHandler(BaseWorkerHandler):
                         image_grid_thw, embeddings_shape, request.request_id
                     )
             lora_request = self._resolve_lora_request(request.model)
+            trace_headers = build_trace_headers(context) if context else None
 
         with time_and_log_code_section(
             f"[DECODE] request: {request.request_id} generate time"
@@ -102,6 +104,7 @@ class MultimodalDecodeWorkerHandler(BaseWorkerHandler):
                 sampling_params=request.sampling_params,
                 request_id=request.request_id,
                 lora_request=lora_request,
+                trace_headers=trace_headers,
             )
 
             rng_first = _nvtx.start_range("mm:decode:first_token", color="darkred")

components/src/dynamo/vllm/multimodal_utils/prefill_worker_utils.py

@@ -140,6 +140,7 @@ async def _fetch_from_encode_workers(
     image_urls: List[str],
     request_id: str,
     receiver: AbstractEmbeddingReceiver,
+    context=None,
 ) -> tuple[List[MultiModalGroup], _PendingRelease | None]:
     """Fan out image URLs to encode workers, load embeddings, and return ready groups.
 
@@ -176,7 +177,7 @@ async def _fetch_from_encode_workers(
                 encode_request.multimodal_inputs = batch
                 payload = encode_request.model_dump_json()
                 encode_response_streams.append(
-                    await encode_worker_client.round_robin(payload)  # type: ignore[arg-type]
+                    await encode_worker_client.round_robin(payload, context=context)  # type: ignore[arg-type]
                 )
                 batch = []
 
@@ -184,7 +185,7 @@ async def _fetch_from_encode_workers(
             encode_request.multimodal_inputs = batch
             payload = encode_request.model_dump_json()
             encode_response_streams.append(
-                await encode_worker_client.round_robin(payload)  # type: ignore[arg-type]
+                await encode_worker_client.round_robin(payload, context=context)  # type: ignore[arg-type]
             )
 
     with time_and_log_code_section(
@@ -223,6 +224,7 @@ async def _fetch_embeddings(
     request_id: str,
     receiver: AbstractEmbeddingReceiver,
     cache: MultimodalEmbeddingCacheManager | None = None,
+    context=None,
 ) -> tuple[list[MultiModalGroup], _PendingRelease | None]:
     """Fetch multimodal embeddings with transparent cache-through.
 
@@ -262,6 +264,7 @@ async def _fetch_embeddings(
             miss_urls,
             request_id,
             receiver,
+            context=context,
         )
 
         # ── 3. Update cache (no-op when cache is None) ──────────────
@@ -293,6 +296,7 @@ async def load_multimodal_embeddings(
     model: str,
     embeddings_dtype: torch.dtype,
     cache: MultimodalEmbeddingCacheManager | None = None,
+    context=None,
 ) -> Dict[str, Any]:
     """Fetch embeddings and build engine-ready ``multi_modal_data``.
 
@@ -307,6 +311,7 @@ async def load_multimodal_embeddings(
         request_id,
         receiver,
         cache=cache,
+        context=context,
     )
 
     multi_modal_data: Dict[str, Any] = defaultdict(list)

components/src/dynamo/vllm/tests/multimodal_handlers/test_vllm_multimodal_pd_worker_handler.py

@@ -299,7 +299,7 @@ class TestGenerateDisagg:
         decode_resp = MagicMock()
         decode_resp.data.return_value = decode_json
 
-        async def fake_round_robin(payload):
+        async def fake_round_robin(payload, context=None):
             async def _stream():
                 yield decode_resp
 

lib/bindings/python/src/dynamo/_core.pyi

@@ -209,19 +209,35 @@ class Client:
         """
         ...
 
-    async def random(self, request: JsonLike) -> AsyncIterator[JsonLike]:
+    async def random(
+            self,
+            request: JsonLike,
+            annotated: bool | None = True,
+            context: Context | None = None,
+        ) -> AsyncIterator[JsonLike]:
         """
         Pick a random instance of the endpoint and issue the request
         """
         ...
 
-    async def round_robin(self, request: JsonLike) -> AsyncIterator[JsonLike]:
+    async def round_robin(
+            self,
+            request: JsonLike,
+            annotated: bool | None = True,
+            context: Context | None = None,
+        ) -> AsyncIterator[JsonLike]:
         """
         Pick the next instance of the endpoint in a round-robin fashion
         """
         ...
 
-    async def direct(self, request: JsonLike, instance: str) -> AsyncIterator[JsonLike]:
+    async def direct(
+            self,
+            request: JsonLike,
+            instance_id: int,
+            annotated: bool | None = True,
+            context: Context | None = None,
+        ) -> AsyncIterator[JsonLike]:
         """
         Pick a specific instance of the endpoint
         """