[MoE] More balanced expert sharding (vllm-project#21497)

Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai>

Author: WoosukKwon

vllm/model_executor/layers/fused_moe/layer.py

@@ -591,22 +591,20 @@ def determine_expert_map(
     if ep_size == 1:
         return (global_num_experts, None)
 
-    local_num_experts = global_num_experts // ep_size
+    # Distribute experts as evenly as possible to each rank.
+    base_experts = global_num_experts // ep_size
+    remainder = global_num_experts % ep_size
+    if ep_rank < remainder:
+        local_num_experts = base_experts + 1
+    else:
+        local_num_experts = base_experts
 
     # Create a tensor of size num_experts filled with -1
     expert_map = torch.full((global_num_experts, ), -1, dtype=torch.int32)
     # Create a expert map for the local experts
-    if ep_rank < (ep_size - 1):
-        # Each non-last rank gets local_num_experts experts.
-        expert_map[ep_rank * local_num_experts:
-                        (ep_rank + 1) * local_num_experts] = \
-            torch.arange(0, local_num_experts, dtype=torch.int32)
-    else:
-        # All remaining experts are assigned to the last rank.
-        local_num_experts = (global_num_experts - ep_rank * local_num_experts)
-
-        expert_map[-local_num_experts:] = \
-            torch.arange(0, local_num_experts, dtype=torch.int32)
+    start_idx = ep_rank * base_experts + min(ep_rank, remainder)
+    expert_map[start_idx:start_idx + local_num_experts] = torch.arange(
+        0, local_num_experts, dtype=torch.int32)
     return (local_num_experts, expert_map)