[#9152][fix] AutoDeploy fused_allreduce_residual_rmsnorm to support demollm mode

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

@@ -40,15 +40,42 @@ def trtllm_allreduce(tensor, op, all_reduce_params=None):
     def fused_allreduce_residual_rmsnorm(
         tensor: torch.Tensor, residual: torch.Tensor, norm_weight: torch.Tensor, eps: float
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Fusing allreduce, residual (add), and hf_rms_norm together."""
-        all_reduce_params = AllReduceParams(
-            fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
-            bias=None,
-            residual=residual,
-            norm_weight=norm_weight,
-            eps=eps,
-        )
-        return trtllm_allreduce(tensor, ReduceOp.SUM, all_reduce_params=all_reduce_params)
+        """Fusing allreduce, residual (add), and hf_rms_norm together.
+
+        When TRT-LLM ops are available (MPI mode), uses the fused kernel.
+        Otherwise, falls back to separate operations using torch distributed.
+        """
+        # Only use TRT-LLM fused op when running with MPI
+        if is_trtllm_op_available():
+            all_reduce_params = AllReduceParams(
+                fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
+                bias=None,
+                residual=residual,
+                norm_weight=norm_weight,
+                eps=eps,
+            )
+            return trtllm_allreduce(tensor, ReduceOp.SUM, all_reduce_params=all_reduce_params)
+        else:
+            # Fallback: unfused implementation using torch distributed
+            # This is used in demollm mode without MPI
+            from .common import all_reduce as torch_all_reduce
+
+            # 1. All-reduce the tensor
+            tensor_reduced = tensor.clone()
+            torch_all_reduce(tensor_reduced, op=ReduceOp.SUM)
+
+            # 2. Add residual
+            tensor_with_residual = tensor_reduced + residual
+
+            # 3. Apply RMSNorm using PyTorch's built-in function
+            norm_out = torch.nn.functional.rms_norm(
+                tensor_with_residual,
+                normalized_shape=(tensor_with_residual.size(-1),),
+                weight=norm_weight,
+                eps=eps,
+            )
+
+            return norm_out, tensor_with_residual
 
     @fused_allreduce_residual_rmsnorm.register_fake
     def fused_allreduce_residual_rmsnorm_fake(