[Performance][B200] Fix deepgemm prologue (#27897)

Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -149,6 +149,15 @@ class FusedMoEPrepareAndFinalize(ABC):
     described above.
     """
 
+    def post_init_setup(self, fused_experts: "FusedMoEPermuteExpertsUnpermute"):
+        """
+        Initialize FusedMoEPrepareAndFinalize settings that depend on
+        FusedMoEPermuteExpertsUnpermute experts object.
+        The FusedMoEPrepareAndFinalize implementations that have such
+        dependencies may choose to override this function.
+        """
+        return
+
     @abstractmethod
     def prepare(
         self,
@@ -503,6 +512,13 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
         """
         raise NotImplementedError
 
+    def supports_packed_ue8m0_act_scales(self) -> bool:
+        """
+        A flag indicating whether or not this class can process packed ue8m0
+        activation scales.
+        """
+        return False
+
     def workspace_dtype(self, act_dtype: torch.dtype) -> torch.dtype:
         """
         Workspace type: The dtype to use for the workspace tensors.
@@ -698,6 +714,8 @@ class FusedMoEModularKernel(torch.nn.Module):
         self.prepare_finalize = prepare_finalize
         self.fused_experts = fused_experts
         self.shared_experts = shared_experts
+
+        self._post_init_setup()
         assert (
             prepare_finalize.activation_format == fused_experts.activation_formats[0]
         ), (
@@ -707,6 +725,13 @@ class FusedMoEModularKernel(torch.nn.Module):
             f"{fused_experts.activation_formats[0]}"
         )
 
+    def _post_init_setup(self):
+        """
+        Resolve any leftover setup dependencies between self.prepare_finalize
+        and self.fused_experts here.
+        """
+        self.prepare_finalize.post_init_setup(self.fused_experts)
+
     def supports_expert_map(self) -> bool:
         """
         A flag indicating whether or not this class supports expert maps.