[MoE Refactor] Integrate Naive Prepare Finalize into MK (#32567)

Signed-off-by: Robert Shaw <robshaw@redhat.com>
Signed-off-by: Amir Klein <203507526+amirkl94@users.noreply.github.com>
Co-authored-by: Robert Shaw <robshaw@redhat.com>
Co-authored-by: amirkl94 <203507526+amirkl94@users.noreply.github.com>

vllm/distributed/device_communicators/all2all.py

@@ -59,7 +59,7 @@ class NaiveAll2AllManager(All2AllManagerBase):
 
         return buffer
 
-    def dispatch(
+    def dispatch_router_logits(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
@@ -84,6 +84,34 @@ class NaiveAll2AllManager(All2AllManagerBase):
 
         return hidden_states, router_logits
 
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        is_sequence_parallel: bool = False,
+        extra_tensors: list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        if extra_tensors is not None:
+            raise NotImplementedError(
+                "extra_tensors is not supported for NaiveAll2AllManager"
+            )
+        sp_size = self.tp_group.world_size if is_sequence_parallel else 1
+        dp_metadata = get_forward_context().dp_metadata
+        assert dp_metadata is not None
+        cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size)
+
+        hidden_states = self.naive_multicast(
+            hidden_states, cu_tokens_across_sp_cpu, is_sequence_parallel
+        )
+        topk_weights = self.naive_multicast(
+            topk_weights, cu_tokens_across_sp_cpu, is_sequence_parallel
+        )
+        topk_ids = self.naive_multicast(
+            topk_ids, cu_tokens_across_sp_cpu, is_sequence_parallel
+        )
+        return hidden_states, topk_weights, topk_ids
+
     def combine(
         self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
     ) -> torch.Tensor:
@@ -114,7 +142,7 @@ class AgRsAll2AllManager(All2AllManagerBase):
     def __init__(self, cpu_group):
         super().__init__(cpu_group)
 
-    def dispatch(
+    def dispatch_router_logits(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
@@ -148,6 +176,46 @@ class AgRsAll2AllManager(All2AllManagerBase):
             return (gathered_tensors[0], gathered_tensors[1], gathered_tensors[2:])
         return gathered_tensors[0], gathered_tensors[1]
 
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        is_sequence_parallel: bool = False,
+        extra_tensors: list[torch.Tensor] | None = None,
+    ) -> (
+        tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+        | tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
+    ):
+        """
+        Gather hidden_states and router_logits from all dp ranks.
+        """
+        dp_metadata = get_forward_context().dp_metadata
+        assert dp_metadata is not None
+        sizes = dp_metadata.get_chunk_sizes_across_dp_rank()
+        assert sizes is not None
+        dist_group = get_ep_group() if is_sequence_parallel else get_dp_group()
+        assert sizes[dist_group.rank_in_group] == hidden_states.shape[0]
+
+        tensors_to_gather = [hidden_states, topk_weights, topk_ids]
+        if extra_tensors is not None:
+            tensors_to_gather.extend(extra_tensors)
+
+        gathered_tensors = dist_group.all_gatherv(
+            tensors_to_gather,
+            dim=0,
+            sizes=sizes,
+        )
+
+        hidden_states = gathered_tensors[0]
+        topk_weights = gathered_tensors[1]
+        topk_ids = gathered_tensors[2]
+
+        if extra_tensors is None:
+            return hidden_states, topk_weights, topk_ids
+
+        return hidden_states, topk_weights, topk_ids, gathered_tensors[3:]
+
     def combine(
         self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
     ) -> torch.Tensor:
@@ -216,7 +284,7 @@ class PPLXAll2AllManager(All2AllManagerBase):
             pplx.AllToAll.internode if self.internode else pplx.AllToAll.intranode,
         )
 
-    def dispatch(
+    def dispatch_router_logits(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
@@ -225,6 +293,19 @@ class PPLXAll2AllManager(All2AllManagerBase):
     ) -> tuple[torch.Tensor, torch.Tensor]:
         raise NotImplementedError
 
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        is_sequence_parallel: bool = False,
+        extra_tensors: list[torch.Tensor] | None = None,
+    ) -> (
+        tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+        | tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
+    ):
+        raise NotImplementedError
+
     def combine(
         self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
     ) -> torch.Tensor:
@@ -264,7 +345,7 @@ class DeepEPAll2AllManagerBase(All2AllManagerBase):
     def get_handle(self, kwargs):
         raise NotImplementedError
 
-    def dispatch(
+    def dispatch_router_logits(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
@@ -273,6 +354,19 @@ class DeepEPAll2AllManagerBase(All2AllManagerBase):
     ) -> tuple[torch.Tensor, torch.Tensor]:
         raise NotImplementedError
 
+    def dispatch(
+        self,
+        hidden_states: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        is_sequence_parallel: bool = False,
+        extra_tensors: list[torch.Tensor] | None = None,
+    ) -> (
+        tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+        | tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
+    ):
+        raise NotImplementedError
+
     def combine(
         self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
     ) -> torch.Tensor: