Fix EPLB algorithm fail to run when using 3 nodes for prefill (#6629)

python/sglang/srt/managers/deepseek_eplb.py

 # This file is copied from https://github.com/deepseek-ai/EPLB/blob/main/eplb.py since that one is not a pypi package
 
-from typing import Literal, Tuple
+from typing import Literal, Optional, Tuple
 
 import torch
 
@@ -257,11 +257,15 @@ def rebalance_experts(
     tokens_per_expert: torch.Tensor,
     num_physical_experts: int,
     num_local_physical_experts: int,
-    num_groups: int,
+    num_groups: Optional[int],
     num_nodes: int,
-    phase: Literal["prefill", "decode"],
+    phase: Literal["prefill", "decode", "null"],
 ):
-    if phase == "prefill":
+    if (
+        (phase == "prefill")
+        and (num_groups is not None)
+        and (num_groups % num_nodes == 0)
+    ):
         return prefill_rebalance_experts(
             tokens_per_expert=tokens_per_expert,
             num_physical_experts=num_physical_experts,
@@ -269,10 +273,8 @@ def rebalance_experts(
             num_groups=num_groups,
             num_nodes=num_nodes,
         )
-    if phase == "decode":
     return decode_rebalance_experts(
         tokens_per_expert=tokens_per_expert,
         num_physical_experts=num_physical_experts,
         num_local_physical_experts=num_local_physical_experts,
     )
-    raise NotImplementedError

python/sglang/srt/managers/expert_location.py

@@ -135,10 +135,6 @@ class ExpertLocationMetadata:
         model_config_for_expert_location = common["model_config_for_expert_location"]
         num_physical_experts = common["num_physical_experts"]
 
-        phase = server_args.disaggregation_mode
-        if phase == "null" or model_config_for_expert_location.num_groups is None:
-            phase = "decode"
-
         physical_to_logical_map, logical_to_all_physical_map, expert_count = (
             deepseek_eplb.rebalance_experts(
                 tokens_per_expert=logical_count,
@@ -146,7 +142,7 @@ class ExpertLocationMetadata:
                 num_local_physical_experts=num_physical_experts // common["ep_size"],
                 num_groups=model_config_for_expert_location.num_groups,
                 num_nodes=server_args.nnodes,
-                phase=phase,
+                phase=server_args.disaggregation_mode,
             )
         )