Fix: NPU AR model runner prefix cache key flattening

vllm_omni/platforms/npu/worker/npu_ar_model_runner.py

@@ -167,7 +167,7 @@ def _maybe_update_prefix_cache(
 
             self.omni_prefix_cache.update_omni_tensor_prefix_cache(
                 hidden_states=hidden_states,
-                multimodal_outputs=multimodal_outputs,
+                multimodal_outputs=flatten_payload(multimodal_outputs) if multimodal_outputs else multimodal_outputs,
                 num_tokens_unpadded=num_tokens_unpadded,
                 slot_mapping=self.input_batch.block_table[0].slot_mapping.cpu,
                 num_tokens_padded=num_tokens_padded,
@@ -190,7 +190,7 @@ def _maybe_get_combined_prefix_cache_tensors(
             combined_multimodal_outputs = self.omni_prefix_cache.get_merged_multimodal_states(
                 query_start_loc=self.query_start_loc.cpu,
                 input_batch=self.input_batch,
-                multimodal_outputs=multimodal_outputs,
+                multimodal_outputs=flatten_payload(multimodal_outputs) if multimodal_outputs else multimodal_outputs,
                 num_scheduled_tokens=num_scheduled_tokens,
             )
         return combined_hidden_states, combined_multimodal_outputs
@@ -944,12 +944,23 @@ def propose_draft_token_ids(sampled_token_ids):
                 mm_payload: dict[str, object] = {}
                 if combined_multimodal_outputs or mm_cpu:
                     if combined_multimodal_outputs:
+                        # Prefix cache enabled; all items have already been processed
+                        # and split apart for each request as needed, and all tensors
+                        # have already been detached to the CPU.  Lists are kept as
+                        # passthrough data for consistent behavior in postprocess.
+                        # Recurse into nested dicts so list-valued sub-keys (e.g.
+                        # embed.tts_bos = [tensor]) are unwrapped to bare tensors
+                        # at the leaves; downstream flatten_payload then yields a
+                        # wire-clean dict[str, torch.Tensor].
+                        def _unwrap_lists(v):
+                            if isinstance(v, list):
+                                return v[idx] if idx < len(v) else v[0]
+                            if isinstance(v, dict):
+                                return {k: _unwrap_lists(sv) for k, sv in v.items()}
+                            return v
+
                         for mm_key in combined_multimodal_outputs.keys():
-                            value = combined_multimodal_outputs[mm_key][rid]
-                            if isinstance(value, list):
-                                mm_payload[mm_key] = value[idx] if idx < len(value) else value[0]
-                            else:
-                                mm_payload[mm_key] = value
+                            mm_payload[mm_key] = _unwrap_lists(combined_multimodal_outputs[mm_key][rid])
                     else:
                         for mm_key, mm_val in mm_cpu.items():
                             mm_payload[mm_key] = to_payload_element(