fix: Fix decode worker in vllm for qwen_vl models (#5281)

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

@@ -18,6 +18,7 @@
     construct_mm_data,
     vLLMMultimodalRequest,
 )
+from ..multimodal_utils.model import construct_qwen_decode_mm_data, is_qwen_vl_model
 
 logger = logging.getLogger(__name__)
 
@@ -63,10 +64,25 @@ async def generate(self, request: vLLMMultimodalRequest, context):
                 request = vLLMMultimodalRequest.model_validate(request)
         logger.debug(f"Received decode request: {{ id: {request.request_id} }}.")
 
-        # Decode worker doesn't process embeddings, so we pass None or empty tensor
+        # For Qwen VL models with mRoPE, we need to pass multi_modal_data containing
+        # image_grid_thw for position embeddings calculation. The decode worker
+        # receives the ORIGINAL unexpanded prompt (with placeholders), and vLLM
+        # will expand it using the multi_modal_data, ensuring the block count
+        # matches what prefill computed.
+        #
+        # We pass unique placeholder embeddings (seeded by request_id) since the
+        # actual embeddings are already in the KV cache from prefill. The unique
+        # values prevent incorrect prefix cache matches between different images.
+        multi_modal_data = None
+        if is_qwen_vl_model(self.config.model):
+            multi_modal_data = construct_qwen_decode_mm_data(
+                request.image_grid_thw, request.embeddings_shape, request.request_id
+            )
+
         gen = self.engine_client.generate(
             prompt=TokensPrompt(
                 prompt_token_ids=request.engine_prompt["prompt_token_ids"],
+                multi_modal_data=multi_modal_data,
             ),
             sampling_params=request.sampling_params,
             request_id=request.request_id,
@@ -222,12 +238,17 @@ async def generate(self, request: vLLMMultimodalRequest, context):
         if self.enable_disagg and self.decode_worker_client:
             decode_request = copy.deepcopy(request)
             async for prefill_response in gen:
-                # Update the prompt token id in the decode request to the one
-                # in response, which has image templated filled in. So that
-                # the decode worker will fetch correct amount of KV blocks.
-                decode_request.engine_prompt[
-                    "prompt_token_ids"
-                ] = prefill_response.prompt_token_ids
+                # For Qwen VL models with mRoPE: Keep the ORIGINAL unexpanded prompt.
+                # The decode worker will pass multi_modal_data which causes vLLM to
+                # expand the prompt identically to prefill, ensuring block counts match.
+                #
+                # For other models: Use the expanded prompt from prefill response.
+                # These models don't pass multi_modal_data in decode, so they need
+                # the already-expanded prompt to match the KV cache layout.
+                if not is_qwen_vl_model(self.config.model):
+                    decode_request.engine_prompt[
+                        "prompt_token_ids"
+                    ] = prefill_response.prompt_token_ids
                 logger.debug(
                     f"Prefill response kv_transfer_params: {prefill_response.kv_transfer_params}"
                 )

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

@@ -177,3 +177,61 @@ def _construct_qwen_image_data(
             "image_grid_thw": grid_thw_tensor,
         }
     }
+
+
+def construct_qwen_decode_mm_data(
+    image_grid_thw: Optional[List[Any]],
+    embeddings_shape: Optional[Any],
+    request_id: str,
+    *,
+    dtype: torch.dtype = torch.float16,
+) -> Dict[str, Dict[str, torch.Tensor]]:
+    """Construct schema-valid Qwen multimodal data for vLLM v1 disagg decode.
+
+    This is a WORKAROUND (WAR) for vLLM's disaggregated multimodal decode limitations.
+
+    Notes:
+    - vLLM parses multimodal inputs and builds `mm_features` from `multi_modal_data`.
+    - For Qwen VL models, the parser enforces that image data contains BOTH
+      `image_embeds` and `image_grid_thw` keys.
+    - In disaggregated decode, the KV cache already includes the vision context
+      from prefill; decode still needs `mm_features` for mRoPE initialization.
+
+    WAR Details:
+    - We generate unique placeholder embeddings based on request_id to prevent
+      incorrect prefix cache matches between different images with same dimensions.
+    - Without this, zero embeddings + same image_grid_thw would create identical
+      cache signatures, causing decode to incorrectly reuse cached KV from
+      different images.
+
+    Caching Caveat:
+    - This WAR disables prefix cache reuse on the DECODE worker (each request
+      has unique placeholder embeddings).
+    - Prefix caching still works correctly on the PREFILL worker, which uses
+      actual image embeddings. This is where the caching benefit matters since
+      prefill does the heavy computation.
+    - Decode receives KV blocks from prefill via NIXL transfer anyway, so
+      decode-side prefix caching provides minimal benefit in disaggregated setup.
+    """
+    if image_grid_thw is None or len(image_grid_thw) == 0:
+        raise ValueError("No image grid provided for Qwen model.")
+    if embeddings_shape is None:
+        raise ValueError("embeddings_shape is required for Qwen decode mm data.")
+
+    # WAR: Use request_id hash as seed for unique placeholder values.
+    # This prevents prefix cache from incorrectly matching different images
+    # that happen to have the same dimensions (same image_grid_thw).
+    seed = hash(request_id) & 0xFFFFFFFF  # Convert to positive 32-bit int
+    generator = torch.Generator().manual_seed(seed)
+    image_embeds = torch.randn(
+        embeddings_shape, dtype=dtype, device="cpu", generator=generator
+    )
+    if image_embeds.ndim == 3:
+        image_embeds = image_embeds.squeeze(0)
+
+    return {
+        "image": {
+            "image_embeds": image_embeds,
+            "image_grid_thw": torch.tensor(image_grid_thw),
+        }
+    }

examples/backends/vllm/launch/disagg_multimodal_epd.sh

@@ -93,7 +93,7 @@ CUDA_VISIBLE_DEVICES=1 python -m dynamo.vllm --multimodal-worker --is-prefill-wo
 # Start decode worker
 echo "Starting decode worker on GPU 2..."
 VLLM_NIXL_SIDE_CHANNEL_PORT=20099 \
-CUDA_VISIBLE_DEVICES=2 python -m dynamo.vllm --multimodal-decode-worker --enable-multimodal --model $MODEL_NAME $EXTRA_ARGS --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20082"}' &
+CUDA_VISIBLE_DEVICES=2 python -m dynamo.vllm --multimodal-decode-worker --enable-multimodal --enable-mm-embeds --model $MODEL_NAME $EXTRA_ARGS --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20082"}' &
 
 echo "=================================================="
 echo "All components started. Waiting for initialization..."