Minor code cleanup refactor for DeepSeek models (#6324)

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -5,6 +5,7 @@ import torch
 from torch.nn import Module
 
 from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 
 try:
     from deep_gemm import (
@@ -40,7 +41,7 @@ from sglang.srt.layers.moe.ep_moe.kernels import (
     tma_align_input_scale,
 )
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
-from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoEMethodBase
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE, FusedMoEMethodBase
 from sglang.srt.layers.moe.topk import select_experts
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
@@ -1173,3 +1174,11 @@ class DeepEPMoE(EPMoE):
         )
 
         return down_output
+
+
+def get_moe_impl_class():
+    if global_server_args_dict["enable_deepep_moe"]:
+        return DeepEPMoE
+    if global_server_args_dict["enable_ep_moe"]:
+        return EPMoE
+    return FusedMoE

python/sglang/srt/models/deepseek_v2.py

@@ -52,7 +52,7 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, EPMoE
+from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, EPMoE, get_moe_impl_class
 from sglang.srt.layers.moe.ep_moe.token_dispatcher import DeepEPDispatcher
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.moe.topk import select_experts
@@ -222,13 +222,7 @@ class DeepseekV2MoE(nn.Module):
 
         self.gate = MoEGate(config=config, prefix=add_prefix("gate", prefix))
 
-        MoEImpl = (
-            DeepEPMoE
-            if global_server_args_dict["enable_deepep_moe"]
-            else (EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE)
-        )
-
-        self.experts = MoEImpl(
+        self.experts = get_moe_impl_class()(
             num_experts=config.n_routed_experts + self.n_share_experts_fusion,
             top_k=config.num_experts_per_tok + min(self.n_share_experts_fusion, 1),
             hidden_size=config.hidden_size,
@@ -251,26 +245,19 @@ class DeepseekV2MoE(nn.Module):
         if config.n_shared_experts is not None and self.n_share_experts_fusion == 0:
             intermediate_size = config.moe_intermediate_size * config.n_shared_experts
             # disable tp for shared experts when enable deepep moe
-            if not global_server_args_dict["enable_deepep_moe"]:
-                self.shared_experts = DeepseekV2MLP(
-                    hidden_size=config.hidden_size,
-                    intermediate_size=intermediate_size,
-                    hidden_act=config.hidden_act,
-                    quant_config=quant_config,
-                    reduce_results=False,
-                    prefix=add_prefix("shared_experts", prefix),
-                )
-            else:
-                self.shared_experts = DeepseekV2MLP(
-                    hidden_size=config.hidden_size,
-                    intermediate_size=intermediate_size,
-                    hidden_act=config.hidden_act,
-                    quant_config=quant_config,
-                    reduce_results=False,
-                    prefix=add_prefix("shared_experts", prefix),
-                    tp_rank=0,
-                    tp_size=1,
-                )
+            self.shared_experts = DeepseekV2MLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=add_prefix("shared_experts", prefix),
+                **(
+                    dict(tp_rank=0, tp_size=1)
+                    if global_server_args_dict["enable_deepep_moe"]
+                    else {}
+                ),
+            )
 
         if global_server_args_dict["enable_deepep_moe"]:
             # TODO: we will support tp < ep in the future
@@ -1726,12 +1713,7 @@ class DeepseekV2ForCausalLM(nn.Module):
 
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
-        MoEImpl = (
-            DeepEPMoE
-            if global_server_args_dict["enable_deepep_moe"]
-            else (EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE)
-        )
-        expert_params_mapping = MoEImpl.make_expert_params_mapping(
+        expert_params_mapping = get_moe_impl_class().make_expert_params_mapping(
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",