[TRTLLM-6747][feat] Merge add sparse exp and shared exp into local re…

tensorrt_llm/_mnnvl_utils.py

@@ -599,6 +599,7 @@ def mnnvl_moe_alltoallv_combine(
         top_k: int,
         token_count: int,
         use_low_precision_combine: bool = False,
+        do_reduce: bool = True,
     ):
         assert x.dim() == 2, "2D tensor supported, please reshape."
         output_tensors = torch.ops.trtllm.moe_comm(
@@ -614,7 +615,8 @@ def mnnvl_moe_alltoallv_combine(
             [True],
             use_low_precision_combine,
         )
-        output_tensor = output_tensors[0]
-        return torch.sum(
-            output_tensor.reshape(token_count, top_k, x.shape[1]), dim=1, keepdim=False
-        )
+        output_tensor = output_tensors[0].reshape(token_count, top_k, x.shape[1])
+        if do_reduce:
+            return torch.sum(output_tensor, dim=1, keepdim=False)
+        else:
+            return output_tensor

tensorrt_llm/_torch/models/modeling_deepseekv3.py

@@ -128,6 +128,12 @@ def weight_dequant(x: torch.Tensor,
     return y
 
 
+@torch.compile(dynamic=True)
+def moe_reduce_add_shared_output(routed_output, shared_output):
+    routed_output = torch.sum(routed_output, dim=1, keepdim=False)
+    return shared_output + routed_output
+
+
 class DeepseekV3MTPHead(nn.Module):
 
     def __init__(self, model_config: ModelConfig[PretrainedConfig]):
@@ -585,6 +591,8 @@ def _compute_routed_output():
                                                        do_finalize)
             return routed_output
 
+        # NOTE: define compiled helpers at module scope to avoid defining decorators inside compiled frames
+
         routed_output, shared_output = maybe_execute_in_parallel(
             _compute_routed_output, _compute_shared_output,
             self.event_dict[EventType.Main],
@@ -593,9 +601,17 @@ def _compute_routed_output():
         if not do_finalize:
             return [shared_output, *routed_output]
         else:
-            assert shared_output.size() == routed_output.size(
-            ), f'unmatched tensor shape'
-            final_hidden_states = shared_output + routed_output
+            if routed_output.dim() == 3:
+                assert shared_output.numel(
+                ) * self.top_k == routed_output.numel(
+                ), 'unmatched tensor shape'
+                final_hidden_states = moe_reduce_add_shared_output(
+                    routed_output, shared_output)
+            else:
+                assert shared_output.size() == routed_output.size(
+                ), 'unmatched tensor shape'
+                final_hidden_states = shared_output + routed_output
+
             if not self.use_dp and self.mapping.tp_size > 1:
                 final_hidden_states = self.allreduce(
                     final_hidden_states,

tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py

@@ -906,7 +906,8 @@ def alltoall_combine(self, final_hidden_states: torch.Tensor,
             ep_size=self.ep_size,
             top_k=top_k,
             token_count=token_count,
-            use_low_precision_combine=self.use_low_precision_combine)
+            use_low_precision_combine=self.use_low_precision_combine,
+            do_reduce=False)
 
         return final_hidden_states