[Moe Refactor] Make Inplace Flag for FusedMoEModularKernel part of the constructor (#33375)

Signed-off-by: Bill Nell <bnell@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -811,11 +811,13 @@ class FusedMoEModularKernel(torch.nn.Module):
         fused_experts: FusedMoEPermuteExpertsUnpermute,
         shared_experts: torch.nn.Module | None = None,
         moe_parallel_config: FusedMoEParallelConfig | None = None,
+        inplace: bool = False,
     ):
         super().__init__()
         self.prepare_finalize = prepare_finalize
         self.fused_experts = fused_experts
         self.shared_experts = shared_experts
+        self.inplace = inplace
 
         # prefer an explicit FusedMoEParallelConfig when available (from
         # FusedMoE layers / tests).
@@ -1292,7 +1294,6 @@ class FusedMoEModularKernel(torch.nn.Module):
         w2: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
-        inplace: bool = False,
         activation: str = "silu",
         global_num_experts: int = -1,
         expert_map: torch.Tensor | None = None,
@@ -1309,8 +1310,6 @@ class FusedMoEModularKernel(torch.nn.Module):
         - topk_weights (torch.Tensor): The topk weights applied at the end of
           the layer.
         - topk_ids (torch.Tensor): A map of row to expert id.
-        - inplace (bool): If True, perform the operation in-place.
-          Defaults to False.
         - activation (str): The activation function to apply after the first
           MoE layer.
         - global_num_experts (int): The total number of experts in the global
@@ -1326,7 +1325,9 @@ class FusedMoEModularKernel(torch.nn.Module):
         - torch.Tensor: The output tensor after applying the MoE layer.
         """
 
-        if inplace and self.shared_experts is None and not disable_inplace():
+        if self.inplace:
+            assert self.shared_experts is None
+            assert not disable_inplace()
             output = hidden_states
         else:
             output = torch.zeros_like(hidden_states)