[v0.11.0] [Bugfix] [MoE]fix error in deepseek when using allgather (#3827)

### What this PR does / why we need it?
After refactoring vllm_ascend/models and FusedMoE, we are unable to pass
`gate` from deepseekv2.py to `AscendFusedMoE.forward`, which will result
in error when running deepseek v3/r1 with allgather.
Hence, this pr removes `gate` related computations from FusedMoE module
in eager/aclgraph mode.
### Does this PR introduce _any_ user-facing change?
`rm_router_logits` is deprecated in eager/aclgraph.
### How was this patch tested?
e2e & ut

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>

vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py

@@ -26,7 +26,7 @@ from vllm.distributed.parallel_state import (
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.fused_moe import FusedMoEConfig
 
-from vllm_ascend.utils import enable_sp, get_rm_router_logits_state
+from vllm_ascend.utils import enable_sp
 
 
 class FusedMoEPrepareAndFinalize(ABC):
@@ -43,31 +43,26 @@ class FusedMoEPrepareAndFinalize(ABC):
 
     def __init__(self, moe_config: FusedMoEConfig):
         self.moe_config = moe_config
-        is_deepseek_v3_r1 = self.moe_config.original_num_experts == 256
-        self.rm_router_logits = get_rm_router_logits_state(
-            self.moe_config.ep_size, self.moe_config.dp_size,
-            is_deepseek_v3_r1)
 
     @abstractmethod
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def prepare(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Prepare tensors before MoE computation. May involve:
           - Padding to align communication boundaries
           - Slicing across tensor-parallel ranks
           - Broadcasting across data-parallel ranks
-          - Recomputing router logits if needed
 
         Args:
             hidden_states (torch.Tensor): Input features, shape [num_tokens, hidden_size]
             router_logits (torch.Tensor): Router outputs, shape [num_tokens, num_experts]
             enable_shared_expert_dp (bool): Skip DP communication for shared experts
             replace_allreduce (bool): Bypass default all-reduce behavior
-            gate (nn.Module, optional): Gate network to recompute router_logits if needed
 
         Returns:
             Tuple of:
@@ -116,12 +111,13 @@ class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
         self.tp_size = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
 
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def prepare(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Preparation steps:
           1. Fetch `mc2_mask` and target padding length from forward context.
@@ -214,12 +210,13 @@ class FusedMoEPrepareAndFinalizeWithAll2All(FusedMoEPrepareAndFinalize):
         self.tp_size = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
 
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def prepare(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Preparation steps:
           1. Pad hidden_states and router_logits to next multiple of TP size.
@@ -307,12 +304,13 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
     TP AG → Attn → TP RS → EP AG → MoE → EP RS
     """
 
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def prepare(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Preparation steps:
           AllGather hidden_states and router_logits to form global tensors.
@@ -325,7 +323,7 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
 
         return self._prepare_with_dp_group(hidden_states, router_logits,
                                            enable_shared_expert_dp,
-                                           replace_allreduce, gate)
+                                           replace_allreduce)
 
     def _prepare_with_ep_group(
         self,
@@ -340,12 +338,12 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
         return hidden_states, router_logits, None
 
     def _prepare_with_dp_group(
-            self,
-            hidden_states: torch.Tensor,
-            router_logits: torch.Tensor,
-            enable_shared_expert_dp: bool = False,
-            replace_allreduce: bool = False,
-            gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Preparation steps:
           1. Fetch max token count across DP group from forward context.
@@ -365,18 +363,14 @@ class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
             if pad_size > 0:
                 hidden_states = nn.functional.pad(hidden_states,
                                                   (0, 0, 0, pad_size))
-                if not self.rm_router_logits:
-                    router_logits = nn.functional.pad(router_logits,
-                                                      (0, 0, 0, pad_size))
+                router_logits = nn.functional.pad(router_logits,
+                                                  (0, 0, 0, pad_size))
 
             # All-gather across DP group
             hidden_states = self.moe_config.dp_group.all_gather(
                 hidden_states, 0)
-            if self.rm_router_logits:
-                router_logits, _ = gate(hidden_states)  # Recompute globally
-            else:
-                router_logits = self.moe_config.dp_group.all_gather(
-                    router_logits, 0)
+            router_logits = self.moe_config.dp_group.all_gather(
+                router_logits, 0)
 
         return hidden_states, router_logits, None
 
@@ -472,12 +466,13 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
             get_dp_group().broadcast(buffer[start:end, :], idx)
         return buffer
 
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def prepare(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+        enable_shared_expert_dp: bool = False,
+        replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Preparation steps:
           1. Fetch cumulative token boundaries from forward context.
@@ -493,11 +488,8 @@ class FusedMoEPrepareAndFinalizeWithNaiveMulticast(FusedMoEPrepareAndFinalize):
             ).dp_metadata.cu_tokens_across_sp(1)
             hidden_states = self._naive_multicast(hidden_states,
                                                   self.cu_tokens_across_dp_cpu)
-            if self.rm_router_logits:
-                router_logits, _ = gate(hidden_states)
-            else:
-                router_logits = self._naive_multicast(
-                    router_logits, self.cu_tokens_across_dp_cpu)
+            router_logits = self._naive_multicast(router_logits,
+                                                  self.cu_tokens_across_dp_cpu)
 
         return hidden_states, router_logits, None
 

vllm_ascend/ops/moe/moe_comm_method.py

@@ -63,15 +63,16 @@ class MoECommMethod(ABC):
         self.fused_moe_prepare_finalize = self._get_fused_moe_prepare_finalize(
         )
 
-    def prepare(self,
-                hidden_states: torch.Tensor,
-                router_logits: torch.Tensor,
-                enable_shared_expert_dp: bool = False,
-                replace_allreduce: bool = False,
-                gate=None) -> tuple[torch.Tensor, torch.Tensor]:
+    def prepare(
+            self,
+            hidden_states: torch.Tensor,
+            router_logits: torch.Tensor,
+            enable_shared_expert_dp: bool = False,
+            replace_allreduce: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         hidden_states, router_logits, mc2_mask = self.fused_moe_prepare_finalize.prepare(
             hidden_states, router_logits, enable_shared_expert_dp,
-            replace_allreduce, gate)
+            replace_allreduce)
         self.mc2_mask = mc2_mask
         return hidden_states, router_logits