[9/N] MoE Refactor: cleanup dispatcher interfaces (#11847)

python/sglang/srt/single_batch_overlap.py

@@ -1,3 +1,19 @@
+# Copyright 2025 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import annotations
+
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Callable, Optional
 
@@ -5,12 +21,12 @@ import torch
 
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.moe import get_moe_runner_backend
+from sglang.srt.layers.moe.topk import TopKOutput
 from sglang.srt.layers.moe.utils import is_sbo_enabled
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import get_int_env_var
 
 if TYPE_CHECKING:
-    from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE
+    from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
 
 class SboFlags:
@@ -54,23 +70,22 @@ class DownGemmOverlapArgs:
 
 def execute_sbo(
     forward_shared_experts: Callable[[], Any],
-    experts: "DeepEPMoE",
+    experts: FusedMoE,
     hidden_states: torch.Tensor,
-    topk_idx: torch.Tensor,
-    topk_weights: torch.Tensor,
-    forward_batch: ForwardBatch,
-    alt_stream: Optional = None,
+    topk_output: TopKOutput,
+    alt_stream: Optional[torch.cuda.Stream] = None,
     disable_sbo: bool = False,
 ):
-    dispatch_output = experts.dispatch(
-        hidden_states, topk_idx, topk_weights, forward_batch
+
+    dispatch_output = experts.dispatcher.dispatch(
+        hidden_states=hidden_states, topk_output=topk_output
     )
 
     combine_overlap_args, down_gemm_overlap_args, meta_overlap_args = (
         _compute_overlap_args(dispatch_output, alt_stream, disable_sbo=disable_sbo)
     )
 
-    hidden_states = experts.moe_impl(
+    hidden_states = experts.run_moe_core(
         dispatch_output, down_gemm_overlap_args=down_gemm_overlap_args
     )
     if (e := meta_overlap_args.get("record_event_after_down")) is not None:
@@ -83,11 +98,10 @@ def execute_sbo(
         ):
             forward_shared_experts()
 
-    hidden_states = experts.combine(
-        hidden_states,
-        dispatch_output.topk_idx,
-        dispatch_output.topk_weights,
-        forward_batch,
+    hidden_states = experts.dispatcher.combine(
+        hidden_states=hidden_states,
+        topk_ids=dispatch_output.topk_ids,
+        topk_weights=dispatch_output.topk_weights,
         overlap_args=combine_overlap_args,
     )
 
@@ -101,9 +115,7 @@ def _compute_overlap_args(dispatch_output, alt_stream, disable_sbo):
     ):
         return None, None, {}
 
-    hidden_states = dispatch_output.hidden_states_fp8
-    if isinstance(hidden_states, tuple):
-        hidden_states = hidden_states[0]
+    hidden_states = dispatch_output.hidden_states
 
     num_local_experts, num_tokens_static, hidden_dim = hidden_states.shape