Cleanup MoE Refactor (#9223)

python/sglang/srt/layers/quantization/mxfp4.py

@@ -573,6 +573,9 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         topk_output: TopKOutput,
         moe_runner_config: MoeRunnerConfig,
     ) -> torch.Tensor:
+
+        from sglang.srt.layers.moe.topk import TopKOutputChecker
+
         if self.use_flashinfer:
             # Based on profiling results, we need to quantize x to mxfp8 here to achieve better performance
             x_quant, x_scale = mxfp8_quantize(
@@ -580,8 +583,10 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             )  # to mxfp8
             x_scale = x_scale.view(torch.float8_e4m3fn).reshape(-1)
             assert x_quant.shape[-1] == self.hidden_size
+            assert TopKOutputChecker.format_is_bypassed(topk_output)
 
-            top_k, router_logits = topk_output
+            top_k = topk_output.topk_config.top_k
+            router_logits = topk_output.router_logits
 
             trtllm_gen_output = trtllm_fp4_block_scale_moe(
                 router_logits.to(torch.bfloat16),
@@ -602,8 +607,8 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 None,  # output2_scale_scalar
                 layer.num_experts,
                 top_k,
-                None,  # n_group
-                None,  # topk_group
+                None,  # n_group      # TODO: support n_group
+                None,  # topk_group   # TODO: support topk_group
                 self.intermediate_size,  # padded to multiple of 256
                 layer.moe_ep_rank * layer.num_local_experts,  # local_expert_offset
                 layer.num_local_experts,  # local num experts

python/sglang/srt/models/deepseek_v2.py

@@ -459,15 +459,15 @@ class DeepseekV2MoE(nn.Module):
         with torch.cuda.stream(self.alt_stream):
             # router_logits: (num_tokens, n_experts)
             router_logits = self.gate(hidden_states)
-            kwargs = {"hidden_states": hidden_states}
-            kwargs["topk_output"] = self.topk(hidden_states, router_logits)
-
-            final_hidden_states = self.experts(**kwargs)
+            topk_output = self.topk(hidden_states, router_logits)
+            final_hidden_states = self.experts(hidden_states, topk_output)
             if not _is_cuda:
                 final_hidden_states *= self.routed_scaling_factor
+
         current_stream.wait_stream(self.alt_stream)
         with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
             final_hidden_states_out = torch.empty_like(final_hidden_states)
+
         torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
         final_hidden_states = final_hidden_states_out
         sm.tag(final_hidden_states)
@@ -489,10 +489,9 @@ class DeepseekV2MoE(nn.Module):
         shared_output = self._forward_shared_experts(hidden_states)
         # router_logits: (num_tokens, n_experts)
         router_logits = self.gate(hidden_states)
-        kwargs = {"hidden_states": hidden_states}
-        kwargs["topk_output"] = self.topk(hidden_states, router_logits)
+        topk_output = self.topk(hidden_states, router_logits)
 
-        final_hidden_states = self.experts(**kwargs)
+        final_hidden_states = self.experts(hidden_states, topk_output)
         if not _is_cuda and not _use_aiter:
             # fused in biased_grouped_topk so we can skip here
             final_hidden_states *= self.routed_scaling_factor

python/sglang/srt/models/glm4_moe.py

@@ -509,9 +509,8 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         with torch.cuda.stream(self.alt_stream):
             # router_logits: (num_tokens, n_experts)
             router_logits = self.gate(hidden_states)
-            kwargs = {"hidden_states": hidden_states}
-            kwargs["topk_output"] = self.topk(hidden_states, router_logits)
-            final_hidden_states = self.experts(**kwargs)
+            topk_output = self.topk(hidden_states, router_logits)
+            final_hidden_states = self.experts(hidden_states, topk_output)
             if not _is_cuda:
                 final_hidden_states *= self.routed_scaling_factor
         current_stream.wait_stream(self.alt_stream)
@@ -552,9 +551,8 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         shared_output = self._forward_shared_experts(hidden_states)
         # router_logits: (num_tokens, n_experts)
         router_logits = self.gate(hidden_states)
-        kwargs = {"hidden_states": hidden_states}
-        kwargs["topk_output"] = self.topk(hidden_states, router_logits)
-        final_hidden_states = self.experts(**kwargs)
+        topk_output = self.topk(hidden_states, router_logits)
+        final_hidden_states = self.experts(hidden_states, topk_output)
         if not _is_cuda and not _use_aiter:
             # fused in biased_grouped_topk so we can skip here
             final_hidden_states *= self.routed_scaling_factor