[Feature] use --eplb_config to set eplb param (#20562)

Signed-off-by: rongfu.leng <rongfu.leng@daocloud.io>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Signed-off-by: rongfu.leng <lenronfu@gmail.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/config/__init__.py

@@ -33,7 +33,8 @@ from vllm.config.cache import (BlockSize, CacheConfig, CacheDType, MambaDType,
                                PrefixCachingHashAlgo)
 from vllm.config.compilation import (CompilationConfig, CompilationLevel,
                                      CUDAGraphMode, PassConfig)
-from vllm.config.parallel import DistributedExecutorBackend, ParallelConfig
+from vllm.config.parallel import (DistributedExecutorBackend, EPLBConfig,
+                                  ParallelConfig)
 from vllm.config.scheduler import SchedulerConfig, SchedulerPolicy
 from vllm.config.utils import ConfigType, config
 from vllm.logger import init_logger

vllm/config/parallel.py

@@ -6,7 +6,7 @@ from dataclasses import field
 from typing import TYPE_CHECKING, Any, Literal, Optional, Union
 
 import torch
-from pydantic import model_validator
+from pydantic import TypeAdapter, model_validator
 from pydantic.dataclasses import dataclass
 from torch.distributed import ProcessGroup, ReduceOp
 from typing_extensions import Self
@@ -32,6 +32,38 @@ logger = init_logger(__name__)
 DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]
 
 
+@config
+@dataclass
+class EPLBConfig:
+    """Configuration for Expert Parallel Load Balancing (EP)."""
+
+    window_size: int = 1000
+    """Window size for expert load recording."""
+    step_interval: int = 3000
+    """
+    Interval for rearranging experts in expert parallelism.
+
+    Note that if this is greater than the EPLB window size, only the metrics
+    of the last `lb_window_size` steps will be used for rearranging experts.
+    """
+
+    num_redundant_experts: int = 0
+    """Number of redundant experts to use for expert parallelism."""
+
+    log_balancedness: bool = False
+    """
+    Log the balancedness each step of expert parallelism.
+    This is turned off by default since it will cause communication overhead.
+    """
+
+    @classmethod
+    def from_cli(cls, cli_value: str) -> "EPLBConfig":
+        """Parse the CLI value for the compilation config.
+        -O1, -O2, -O3, etc. is handled in FlexibleArgumentParser.
+        """
+        return TypeAdapter(EPLBConfig).validate_json(cli_value)
+
+
 @config
 @dataclass
 class ParallelConfig:
@@ -75,22 +107,24 @@ class ParallelConfig:
     """Use expert parallelism instead of tensor parallelism for MoE layers."""
     enable_eplb: bool = False
     """Enable expert parallelism load balancing for MoE layers."""
-    num_redundant_experts: int = 0
-    """Number of redundant experts to use for expert parallelism."""
-    eplb_window_size: int = 1000
-    """Window size for expert load recording."""
-    eplb_step_interval: int = 3000
-    """
-    Interval for rearranging experts in expert parallelism.
-
-    Note that if this is greater than the EPLB window size, only the metrics
-    of the last `eplb_window_size` steps will be used for rearranging experts.
-    """
-    eplb_log_balancedness: bool = False
-    """
-    Log the balancedness each step of expert parallelism.
-    This is turned off by default since it will cause communication overhead.
-    """
+    eplb_config: EPLBConfig = field(default_factory=EPLBConfig)
+    """Expert parallelism configuration."""
+    num_redundant_experts: Optional[int] = None
+    """`num_redundant_experts` is deprecated and has been replaced with
+    `eplb_config.num_redundant_experts`. This will be removed in v0.12.0.
+    Please use `eplb_config.num_redundant_experts` instead."""
+    eplb_window_size: Optional[int] = None
+    """`eplb_window_size` is deprecated and has been replaced with
+    `eplb_config.window_size`. This will be removed in v0.12.0.
+    Please use `eplb_config.window_size` instead."""
+    eplb_step_interval: Optional[int] = None
+    """`eplb_step_interval` is deprecated and has been replaced with
+    `eplb_config.step_interval`. This will be removed in v0.12.0.
+    Please use `eplb_config.step_interval` instead."""
+    eplb_log_balancedness: Optional[bool] = None
+    """`eplb_log_balancedness` is deprecated and has been replaced with
+    `eplb_config.log_balancedness`. This will be removed in v0.12.0.
+    Please use `eplb_config.log_balancedness` instead."""
 
     max_parallel_loading_workers: Optional[int] = None
     """Maximum number of parallel loading workers when loading model
@@ -237,6 +271,38 @@ class ParallelConfig:
         return hashlib.sha256(str(factors).encode()).hexdigest()
 
     def __post_init__(self) -> None:
+        # Forward deprecated fields to their new location
+        if self.num_redundant_experts is not None:
+            self.eplb_config.num_redundant_experts = (
+                self.num_redundant_experts)
+            logger.warning_once(
+                "num_redundant_experts is deprecated and has been replaced "
+                "with eplb_config.num_redundant_experts. This will be removed "
+                "in v0.12.0. Changing this field after initialization will "
+                "have no effect.")
+        if self.eplb_window_size is not None:
+            self.eplb_config.window_size = self.eplb_window_size
+            logger.warning_once(
+                "eplb_window_size is deprecated and has been replaced "
+                "with eplb_config.window_size. This will be removed "
+                "in v0.12.0. Changing this field after initialization will "
+                "have no effect.")
+        if self.eplb_step_interval is not None:
+            self.eplb_config.step_interval = self.eplb_step_interval
+            logger.warning_once(
+                "eplb_step_interval is deprecated and has been replaced "
+                "with eplb_config.step_interval. This will be removed "
+                "in v0.12.0. Changing this field after initialization will "
+                "have no effect.")
+        if self.eplb_log_balancedness is not None:
+            self.eplb_config.log_balancedness = self.eplb_log_balancedness
+            logger.warning_once(
+                "eplb_log_balancedness is deprecated and has been replaced "
+                "with eplb_config.log_balancedness. This will be removed "
+                "in v0.12.0. Changing this field after initialization will "
+                "have no effect.")
+
+        # Continue with the rest of the initialization
         self.world_size = self.pipeline_parallel_size * \
             self.tensor_parallel_size
 
@@ -275,10 +341,10 @@ class ParallelConfig:
                 raise ValueError(
                     "Expert parallelism load balancing is only supported on "
                     "CUDA devices now.")
-            if self.num_redundant_experts < 0:
+            if self.eplb_config.num_redundant_experts < 0:
                 raise ValueError(
                     "num_redundant_experts must be non-negative, but got "
-                    f"{self.num_redundant_experts}.")
+                    f"{self.eplb_config.num_redundant_experts}.")
             if not self.enable_expert_parallel:
                 raise ValueError(
                     "enable_expert_parallel must be True to use EPLB.")
@@ -289,10 +355,10 @@ class ParallelConfig:
                     f"TP={self.tensor_parallel_size},DP={self.data_parallel_size}."
                 )
         else:
-            if self.num_redundant_experts != 0:
+            if self.eplb_config.num_redundant_experts != 0:
                 raise ValueError(
                     "num_redundant_experts should be used with EPLB."
-                    f"{self.num_redundant_experts}.")
+                    f"{self.eplb_config.num_redundant_experts}.")
         if self.distributed_executor_backend is None and self.world_size > 1:
             # We use multiprocessing by default if world_size fits on the
             # current node and we aren't in a ray placement group.

vllm/distributed/eplb/eplb_state.py

@@ -244,7 +244,7 @@ class EplbState:
             dtype=torch.int32,
             device=device,
         )
-        expert_load_window_size = parallel_config.eplb_window_size
+        expert_load_window_size = parallel_config.eplb_config.window_size
         expert_load_window = torch.zeros(
             (expert_load_window_size, model.num_moe_layers,
              model.num_physical_experts),
@@ -253,7 +253,7 @@ class EplbState:
         )
 
         # Set the initial progress of rearrangement to 3/4
-        eplb_step_interval = parallel_config.eplb_step_interval
+        eplb_step_interval = parallel_config.eplb_config.step_interval
         expert_rearrangement_step = max(
             0, eplb_step_interval - eplb_step_interval // 4)
 

vllm/engine/arg_utils.py

@@ -25,7 +25,7 @@ import vllm.envs as envs
 from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
                          ConfigFormat, ConfigType, ConvertOption,
                          DecodingConfig, DetailedTraceModules, Device,
-                         DeviceConfig, DistributedExecutorBackend,
+                         DeviceConfig, DistributedExecutorBackend, EPLBConfig,
                          GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                          KVTransferConfig, LoadConfig, LogprobsMode,
                          LoRAConfig, MambaDType, MMEncoderTPMode, ModelConfig,
@@ -305,11 +305,12 @@ class EngineArgs:
     data_parallel_hybrid_lb: bool = False
     data_parallel_backend: str = ParallelConfig.data_parallel_backend
     enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
+    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
     enable_eplb: bool = ParallelConfig.enable_eplb
-    num_redundant_experts: int = ParallelConfig.num_redundant_experts
-    eplb_window_size: int = ParallelConfig.eplb_window_size
-    eplb_step_interval: int = ParallelConfig.eplb_step_interval
-    eplb_log_balancedness: bool = ParallelConfig.eplb_log_balancedness
+    num_redundant_experts: int = EPLBConfig.num_redundant_experts
+    eplb_window_size: int = EPLBConfig.window_size
+    eplb_step_interval: int = EPLBConfig.step_interval
+    eplb_log_balancedness: bool = EPLBConfig.log_balancedness
     max_parallel_loading_workers: Optional[
         int] = ParallelConfig.max_parallel_loading_workers
     block_size: Optional[BlockSize] = CacheConfig.block_size
@@ -454,6 +455,9 @@ class EngineArgs:
         if isinstance(self.compilation_config, dict):
             self.compilation_config = CompilationConfig(
                 **self.compilation_config)
+        if isinstance(self.eplb_config, dict):
+            self.eplb_config = EPLBConfig.from_cli(json.dumps(
+                self.eplb_config))
         # Setup plugins
         from vllm.plugins import load_general_plugins
         load_general_plugins()
@@ -661,14 +665,32 @@ class EngineArgs:
             **parallel_kwargs["enable_expert_parallel"])
         parallel_group.add_argument("--enable-eplb",
                                     **parallel_kwargs["enable_eplb"])
-        parallel_group.add_argument("--num-redundant-experts",
-                                    **parallel_kwargs["num_redundant_experts"])
-        parallel_group.add_argument("--eplb-window-size",
-                                    **parallel_kwargs["eplb_window_size"])
-        parallel_group.add_argument("--eplb-step-interval",
-                                    **parallel_kwargs["eplb_step_interval"])
-        parallel_group.add_argument("--eplb-log-balancedness",
-                                    **parallel_kwargs["eplb_log_balancedness"])
+        parallel_group.add_argument("--eplb-config",
+                                    **parallel_kwargs["eplb_config"])
+        parallel_group.add_argument(
+            "--num-redundant-experts",
+            type=int,
+            help=
+            "[DEPRECATED] --num-redundant-experts will be removed in v0.12.0.",
+            deprecated=True)
+        parallel_group.add_argument(
+            "--eplb-window-size",
+            type=int,
+            help="[DEPRECATED] --eplb-window-size will be removed in v0.12.0.",
+            deprecated=True)
+        parallel_group.add_argument(
+            "--eplb-step-interval",
+            type=int,
+            help=
+            "[DEPRECATED] --eplb-step-interval will be removed in v0.12.0.",
+            deprecated=True)
+        parallel_group.add_argument(
+            "--eplb-log-balancedness",
+            action=argparse.BooleanOptionalAction,
+            help=
+            "[DEPRECATED] --eplb-log-balancedness will be removed in v0.12.0.",
+            deprecated=True)
+
         parallel_group.add_argument(
             "--max-parallel-loading-workers",
             **parallel_kwargs["max_parallel_loading_workers"])
@@ -1244,6 +1266,16 @@ class EngineArgs:
                     "Currently, speculative decoding is not supported with "
                     "async scheduling.")
 
+        # Forward the deprecated CLI args to the EPLB config.
+        if self.num_redundant_experts is not None:
+            self.eplb_config.num_redundant_experts = self.num_redundant_experts
+        if self.eplb_window_size is not None:
+            self.eplb_config.window_size = self.eplb_window_size
+        if self.eplb_step_interval is not None:
+            self.eplb_config.step_interval = self.eplb_step_interval
+        if self.eplb_log_balancedness is not None:
+            self.eplb_config.log_balancedness = self.eplb_log_balancedness
+
         parallel_config = ParallelConfig(
             pipeline_parallel_size=self.pipeline_parallel_size,
             tensor_parallel_size=self.tensor_parallel_size,
@@ -1257,10 +1289,7 @@ class EngineArgs:
             data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
             enable_expert_parallel=self.enable_expert_parallel,
             enable_eplb=self.enable_eplb,
-            num_redundant_experts=self.num_redundant_experts,
-            eplb_window_size=self.eplb_window_size,
-            eplb_step_interval=self.eplb_step_interval,
-            eplb_log_balancedness=self.eplb_log_balancedness,
+            eplb_config=self.eplb_config,
             max_parallel_loading_workers=self.max_parallel_loading_workers,
             disable_custom_all_reduce=self.disable_custom_all_reduce,
             ray_workers_use_nsight=self.ray_workers_use_nsight,

vllm/model_executor/models/deepseek_v2.py

@@ -132,10 +132,10 @@ class DeepseekV2MoE(nn.Module):
 
         # Load balancing settings.
         vllm_config = get_current_vllm_config()
-        parallel_config = vllm_config.parallel_config
+        eplb_config = vllm_config.parallel_config.eplb_config
         self.enable_eplb = enable_eplb
 
-        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
         self.n_logical_experts = self.n_routed_experts
         self.n_physical_experts = (self.n_logical_experts +
                                    self.n_redundant_experts)

vllm/model_executor/models/glm4_moe.py

@@ -131,10 +131,10 @@ class Glm4MoE(nn.Module):
 
         # Load balancing settings.
         vllm_config = get_current_vllm_config()
-        parallel_config = vllm_config.parallel_config
+        eplb_config = vllm_config.parallel_config.eplb_config
         self.enable_eplb = enable_eplb
 
-        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
         self.n_logical_experts = self.n_routed_experts
         self.n_physical_experts = (self.n_logical_experts +
                                    self.n_redundant_experts)

vllm/model_executor/models/qwen3_moe.py

@@ -121,11 +121,11 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
 
         # Load balancing settings.
         vllm_config = get_current_vllm_config()
-        parallel_config = vllm_config.parallel_config
+        eplb_config = vllm_config.parallel_config.eplb_config
         self.enable_eplb = enable_eplb
 
         self.n_logical_experts = self.n_routed_experts
-        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
         self.n_physical_experts = (self.n_logical_experts +
                                    self.n_redundant_experts)
         self.n_local_physical_experts = self.n_physical_experts // self.ep_size
@@ -363,7 +363,8 @@ class Qwen3MoeModel(nn.Module):
         quant_config = vllm_config.quant_config
         parallel_config = vllm_config.parallel_config
         enable_eplb = parallel_config.enable_eplb
-        self.num_redundant_experts = parallel_config.num_redundant_experts
+        eplb_config = parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
 
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size

vllm/v1/worker/gpu_model_runner.py

@@ -1435,7 +1435,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             model,
             is_dummy,
             is_profile,
-            log_stats=self.parallel_config.eplb_log_balancedness,
+            log_stats=self.parallel_config.eplb_config.log_balancedness,
         )
 
     def get_dp_padding(self,
@@ -1977,7 +1977,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             global_expert_load, old_global_expert_indices = (
                 EplbState.recv_state())
             num_logical_experts = global_expert_load.shape[1]
-            self.parallel_config.num_redundant_experts = (
+            self.parallel_config.eplb_config.num_redundant_experts = (
                 num_local_physical_experts * new_ep_size - num_logical_experts)
             assert old_global_expert_indices.shape[
                 1] % num_local_physical_experts == 0

vllm/v1/worker/gpu_worker.py

@@ -515,7 +515,7 @@ class Worker(WorkerBase):
             assert self.model_runner.eplb_state is not None
             new_physical_experts = \
                 self.model_runner.eplb_state.physical_to_logical_map.shape[1]
-            parallel_config.num_redundant_experts = (
+            parallel_config.eplb_config.num_redundant_experts = (
                 new_physical_experts -
                 self.model_runner.eplb_state.logical_replica_count.shape[1])
             global_expert_load = None
@@ -531,7 +531,7 @@ class Worker(WorkerBase):
             assert self.model_runner.eplb_state is not None
             global_expert_load = self.model_runner.eplb_state.rearrange(
                 self.model_runner.model, execute_shuffle=False)
-            parallel_config.num_redundant_experts = (
+            parallel_config.eplb_config.num_redundant_experts = (
                 new_physical_experts - global_expert_load.shape[1])
         prepare_communication_buffer_for_model(self.model_runner.model)
         self.model_runner.model.update_physical_experts_metadata(