[Feature] Support redundant expert for eplb (#5918)

* [BugFix] support redundant expert for eplb

* support redundant expert for eplb

* support redundant expert for eplb

* update

* fix ci eplb

custom_ops/gpu_ops/moe/ep_moe_expert_dispatch.cu

@@ -48,6 +48,11 @@
       __VA_ARGS__                                                              \
       break;                                                                   \
     }                                                                          \
+    case 7: {                                                                  \
+      constexpr size_t NUM_EXPERTS_PER_RANK = 7;                               \
+      __VA_ARGS__                                                              \
+      break;                                                                   \
+    }                                                                          \
     case 8: {                                                                  \
       constexpr size_t NUM_EXPERTS_PER_RANK = 8;                               \
       __VA_ARGS__                                                              \
@@ -68,6 +73,11 @@
       __VA_ARGS__                                                              \
       break;                                                                   \
     }                                                                          \
+    case 17: {                                                                 \
+      constexpr size_t NUM_EXPERTS_PER_RANK = 17;                              \
+      __VA_ARGS__                                                              \
+      break;                                                                   \
+    }                                                                          \
     case 20: {                                                                 \
       constexpr size_t NUM_EXPERTS_PER_RANK = 20;                              \
       __VA_ARGS__                                                              \

fastdeploy/entrypoints/engine_client.py

@@ -607,6 +607,7 @@ class EngineClient:
         Returns:
             tuple: response body, status code
         """
+        content, status_code = None, HTTPStatus.OK
         eplb_config = self.fd_config.eplb_config
         if not eplb_config.enable_eplb:
             content = {"code": 1, "msg": "redundant expert is disabled"}
@@ -709,6 +710,7 @@ class EngineClient:
         Returns:
             tuple: response body, status code
         """
+        content, status_code = None, HTTPStatus.OK
         eplb_config = self.fd_config.eplb_config
         if not eplb_config.enable_eplb:
             content = {"code": 1, "msg": "redundant expert is disabled"}

fastdeploy/model_executor/layers/backends/xpu/moe/ep.py

@@ -277,7 +277,7 @@ class XPUEPRunner:
                 moe_topk=self.top_k,
                 apply_norm_weight=True,  # apply_norm_weight
                 enable_softmax_top_k_fused=False,
-                redundant_ep_rank_num_plus_one=layer.fd_config.model_config.redundant_experts_num + 1,
+                redundant_ep_rank_num_plus_one=layer.fd_config.eplb_config.redundant_experts_num + 1,
             )
         else:
             topk_idx, topk_weights = fastdeploy.model_executor.ops.xpu.moe_topk_select(

fastdeploy/model_executor/layers/moe/ep.py

@@ -472,7 +472,7 @@ class EPRunner:
                     expert_id_to_ep_rank_array=expert_id_to_ep_rank_array,
                     expert_in_rank_num_list=expert_in_rank_num_list,
                     tokens_per_expert_stats_list=tokens_per_expert_stats_list,
-                    redundant_ep_rank_num_plus_one=layer.fd_config.model_config.redundant_experts_num + 1,
+                    redundant_ep_rank_num_plus_one=layer.fd_config.eplb_config.redundant_experts_num + 1,
                 )
             else:
                 topk_idx, topk_weights = fastdeploy.model_executor.ops.gpu.moe_redundant_topk_select(
@@ -484,7 +484,7 @@ class EPRunner:
                     moe_topk=self.top_k,
                     apply_norm_weight=True,
                     enable_softmax_top_k_fused=False,
-                    redundant_ep_rank_num_plus_one=layer.fd_config.model_config.redundant_experts_num + 1,
+                    redundant_ep_rank_num_plus_one=layer.fd_config.eplb_config.redundant_experts_num + 1,
                 )
         else:
             if layer.topk_method == "noaux_tc":

fastdeploy/model_executor/layers/moe/fused_moe_backend_base.py

@@ -84,7 +84,7 @@ class MoEMethodBase(QuantMethodBase):
             "num_max_dispatch_tokens_per_rank": layer.fd_config.model_config.num_max_dispatch_tokens_per_rank,
             "ep_size": layer.ep_size,
             "ep_rank": layer.ep_rank,
-            "redundant_experts_num": layer.fd_config.model_config.redundant_experts_num,
+            "redundant_experts_num": layer.fd_config.eplb_config.redundant_experts_num,
             "ep_group": layer.fd_config.parallel_config.ep_group,
         }
 

fastdeploy/model_executor/layers/moe/moe.py

@@ -467,13 +467,18 @@ class FusedMoE(nn.Layer):
         """
         logical_expert_ids = [
             i
+            % (
+                self.fd_config.model_config.moe_num_experts[0]
+                if isinstance(self.fd_config.model_config.moe_num_experts, list)
+                else self.fd_config.model_config.moe_num_experts
+            )
             for i in range(
                 self.expert_id_offset,
                 self.expert_id_offset + self.num_local_experts,
             )
         ]
         ep_rank_to_expert_id_list = [i for i in range(self.num_experts)]
-        if self.redundant_table_manger is not None and is_rearrange is True:
+        if self.redundant_table_manger is not None:
             (
                 ep_rank_to_expert_id_list,
                 expert_id_to_ep_rank_array,
@@ -487,10 +492,7 @@ class FusedMoE(nn.Layer):
         down_proj_weights = []
         if isinstance(state_dict, list):
             state_dict = dict(state_dict)
-        is_ffn_merged = (
+        is_ffn_merged = up_gate_proj_expert_weight_key.format(logical_expert_ids[0]) in state_dict
-            up_gate_proj_expert_weight_key.format(logical_expert_ids[0] if is_rearrange else self.expert_id_offset)
-            in state_dict
-        )
         if is_ffn_merged:
             for expert_idx in logical_expert_ids:
                 down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
@@ -498,7 +500,7 @@ class FusedMoE(nn.Layer):
                 up_gate_proj_weights.append(
                     get_tensor(
                         (
-                            state_dict.pop(up_gate_proj_expert_weight_key_name)
+                            state_dict[up_gate_proj_expert_weight_key_name]
                             if up_gate_proj_expert_weight_key_name in state_dict
                             else up_gate_proj_expert_weight_key_name
                         ),
@@ -508,7 +510,7 @@ class FusedMoE(nn.Layer):
                 down_proj_weights.append(
                     get_tensor(
                         (
-                            state_dict.pop(down_proj_expert_weight_key_name)
+                            state_dict[down_proj_expert_weight_key_name]
                             if down_proj_expert_weight_key_name in state_dict
                             else down_proj_expert_weight_key_name
                         ),

fastdeploy/model_executor/load_weight_utils.py

@@ -252,8 +252,13 @@ def load_ep_checkpoint(cls: PretrainedModel, model_path: str, fd_config: FDConfi
         "mtp_block" if getattr(fd_config.speculative_config, "model_type", "main") == "mtp" else "layers"
     )
 
+    moe_num_experts = fd_config.model_config.moe_num_experts
+    if isinstance(moe_num_experts, list):
+        moe_num_experts = moe_num_experts[0]
     for i in range(fd_config.model_config.moe_layer_start_index, fd_config.model_config.num_hidden_layers):
         for j in get_expert_ranges(fd_config):
+            # Map redundant expert IDs back to actual expert IDs for weight loading
+            j = j % moe_num_experts
             up_gate_proj_key = f"ernie.{prefix_layer_name}.{i}.mlp.experts.{j}.up_gate_proj.weight"
             down_proj_key = f"ernie.{prefix_layer_name}.{i}.mlp.experts.{j}.down_proj.weight"
 

fastdeploy/model_executor/models/ernie4_5_moe.py

@@ -400,7 +400,7 @@ class Ernie4_5_Model(nn.Layer):
             self.redundant_table_manger = RedundantExpertManger(
                 n_routed_experts=fd_config.model_config.moe_num_experts,
                 num_hidden_layers=fd_config.model_config.num_hidden_layers,
-                redundant_experts_num=fd_config.model_config.redundant_experts_num,
+                redundant_experts_num=fd_config.eplb_config.redundant_experts_num,
                 ep_size=fd_config.parallel_config.expert_parallel_size,
             )
 

fastdeploy/worker/experts_manager.py

@@ -42,7 +42,7 @@ class RedundantExpertManger:
         self.num_hidden_layers = num_hidden_layers
 
         self.num_replicas = self.num_expert + self.redundant_experts_num
-        self.num_nodes = max(ep_size // 8, 1)
+        self.num_nodes = max(ep_size // 8, 8)
         self.num_gpus = ep_size
         self.num_groups = 1
 

fastdeploy/worker/worker_process.py

@@ -927,6 +927,7 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
     parallel_config = ParallelConfig(vars(args))
     cache_config = CacheConfig(vars(args))
     scheduler_config = SchedulerConfig(vars(args))
+    eplb_config = EPLBConfig(args.eplb_config)
 
     parallel_config.tensor_parallel_rank = local_rank % parallel_config.tensor_parallel_size
     parallel_config.data_parallel_rank = local_rank // parallel_config.tensor_parallel_size
@@ -940,9 +941,9 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
     if parallel_config.expert_parallel_size > 1:
         expert_parallel_rank = int(local_rank % parallel_config.expert_parallel_size)
         if isinstance(model_config.moe_num_experts, list):
-            num_experts = model_config.moe_num_experts[0]
+            num_experts = model_config.moe_num_experts[0] + eplb_config.redundant_experts_num
         else:
-            num_experts = model_config.moe_num_experts
+            num_experts = model_config.moe_num_experts + eplb_config.redundant_experts_num
         num_experts_per_rank = num_experts // parallel_config.expert_parallel_size
         num_experts_start_offset = expert_parallel_rank * num_experts_per_rank
         parallel_config.expert_parallel_rank = expert_parallel_rank
@@ -958,7 +959,6 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
     plas_attention_config = PlasAttentionConfig(args.plas_attention_config)
 
     early_stop_config = EarlyStopConfig(args.early_stop_config)
-    eplb_config = EPLBConfig(args.eplb_config)
 
     structured_outputs_config: StructuredOutputsConfig = StructuredOutputsConfig(args=vars(args))
     routing_replay_config = RoutingReplayConfig(args.routing_replay_config)

tests/model_executor/test_ep.py

@@ -460,7 +460,7 @@ def test_eprunner_moe_select_redundant_and_topk(monkeypatch):
         top_k=2,
         routed_scaling_factor=1.0,
         gate_correction_bias=None,
-        fd_config=SimpleNamespace(model_config=SimpleNamespace(redundant_experts_num=0)),
+        fd_config=SimpleNamespace(eplb_config=SimpleNamespace(redundant_experts_num=0)),
     )
     gate_out = paddle.randn([1, 4], dtype="float32")