[2/N] Added the core structure of elastic EP and the eplb algorithm with faulty rank

python/sglang/srt/elastic_ep/elastic_ep.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+from sglang.srt.managers.schedule_batch import ServerArgs
+from sglang.srt.utils import is_cpu, is_cuda
+
+
+@dataclass
+class ElasticEPState:
+    active_ranks: Optional[torch.Tensor]
+    last_active_ranks: Optional[torch.Tensor]
+    active_ranks_cpu: Optional[torch.Tensor]
+
+    def is_active_equal_last(self) -> bool:
+        return torch.equal(self.active_ranks, self.last_active_ranks)
+
+    def sync_active_to_cpu(self):
+        if self.active_ranks is not None:
+            self.active_ranks_cpu = self.active_ranks.detach().cpu().clone()
+
+    def snapshot_active_to_last(self):
+        if self.active_ranks is not None:
+            self.last_active_ranks = self.active_ranks.clone()
+
+
+class ElasticEPStateManager:
+    _instance: Optional[ElasticEPState] = None
+
+    @classmethod
+    def instance(cls) -> ElasticEPState:
+        return cls._instance
+
+    @classmethod
+    def init(cls, server_args: ServerArgs):
+        if cls._instance is not None:
+            return cls._instance
+
+        if server_args.elastic_ep_backend is not None:
+            cls._instance = cls._build_state(ep_size=None, device=None)
+        return cls._instance
+
+    @staticmethod
+    def _select_device() -> torch.device:
+        if is_cuda():
+            return torch.device("cuda")
+        elif is_cpu():
+            return torch.device("cpu")
+        else:
+            raise NotImplementedError("Only CUDA and CPU support elastic ep now.")
+
+    @classmethod
+    def _build_state(
+        cls, *, ep_size: Optional[int] = None, device: Optional[torch.device] = None
+    ) -> ElasticEPState:
+
+        active = cls.healthy_rank_state(ep_size=ep_size, device=device)
+        return ElasticEPState(
+            active_ranks=active,
+            last_active_ranks=active.clone(),
+            active_ranks_cpu=active.detach().cpu().clone(),
+        )
+
+    @classmethod
+    def healthy_rank_state(
+        cls, *, ep_size: Optional[int] = None, device: Optional[torch.device] = None
+    ) -> torch.Tensor:
+        size = ep_size if ep_size is not None else torch.distributed.get_world_size()
+        dev = device if device is not None else cls._select_device()
+
+        return torch.ones(size, dtype=torch.int32, device=dev)

python/sglang/srt/eplb/eplb_algorithms/__init__.py

@@ -3,14 +3,16 @@
 
 import torch
 
-from sglang.srt.eplb.eplb_algorithms import deepseek, deepseek_vec
+from sglang.srt.elastic_ep.elastic_ep import ElasticEPStateManager
+from sglang.srt.eplb.eplb_algorithms import deepseek, deepseek_vec, elasticity_aware
 
 
 class EplbAlgorithm(Enum):
     deepseek = auto()
     deepseek_hierarchical = auto()
     deepseek_vec = auto()
     deepseek_vec_hierarchical = auto()
+    elasticity_aware = auto()
     # TODO may have more algorithm later
 
 
@@ -45,6 +47,21 @@ def rebalance_experts(
             enable_hierarchical=algorithm == EplbAlgorithm.deepseek_vec_hierarchical,
         )
 
+    if algorithm == EplbAlgorithm.elasticity_aware:
+        return elasticity_aware.rebalance_experts(
+            weight=tokens_per_expert.sum(dim=0),
+            num_replicas=num_physical_experts,
+            num_groups=num_groups,
+            num_nodes=num_nodes,
+            num_gpus=num_physical_experts // num_local_physical_experts,
+            enable_hierarchical=True,
+            active_ranks=(
+                ElasticEPStateManager.instance().active_ranks
+                if ElasticEPStateManager.instance() is not None
+                else ElasticEPStateManager.healthy_rank_state()
+            ),
+        )
+
     raise NotImplementedError
 
 

python/sglang/srt/eplb/eplb_algorithms/elasticity_aware.py

@@ -0,0 +1,87 @@
+from typing import Tuple
+
+import torch
+
+from sglang.srt.eplb.eplb_algorithms.deepseek import rebalance_experts_hierarchical
+
+
+def rebalance_experts(
+    weight: torch.Tensor,
+    num_replicas: int,
+    num_groups: int,
+    num_nodes: int,
+    num_gpus: int,
+    enable_hierarchical: bool,
+    active_ranks: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Entry point for expert-parallelism load balancer.
+
+    Parameters:
+        weight: [layers, num_logical_experts], the load statistics for all logical experts
+        num_replicas: number of physical experts, must be a multiple of `num_gpus`
+        num_groups: number of expert groups
+        num_nodes: number of server nodes, where the intra-node network (e.g, NVLink) is faster
+        num_gpus: number of GPUs, must be a multiple of `num_nodes`
+
+    Returns:
+        physical_to_logical_map: [layers, num_replicas], the expert index of each replica
+        logical_to_physical_map: [layers, num_logical_experts, X], the replica indices for each expert
+        expert_count: [layers, num_logical_experts], number of physical replicas for each logical expert
+    """
+
+    num_layers, num_logical_experts = weight.shape
+    weight = weight.float().cpu()
+    num_active_ranks = active_ranks.sum().item()
+    num_local_experts = num_replicas // num_gpus
+    if num_active_ranks < num_gpus:
+        # Must fall back to global load-balance policy
+        # and fix some params
+        phy2log, phyrank, logcnt = rebalance_experts_hierarchical(
+            weight,
+            num_local_experts * num_active_ranks,
+            1,
+            1,
+            num_active_ranks,
+        )
+    elif enable_hierarchical:
+        # use hierarchical load-balance policy
+        phy2log, phyrank, logcnt = rebalance_experts_hierarchical(
+            weight, num_replicas, num_groups, num_nodes, num_gpus
+        )
+    else:
+        # use global load-balance policy
+        phy2log, phyrank, logcnt = rebalance_experts_hierarchical(
+            weight, num_replicas, 1, 1, num_gpus
+        )
+    maxlogcnt = logcnt.max().item()
+    log2phy: torch.Tensor = torch.full(
+        (num_layers, num_logical_experts, maxlogcnt),
+        -1,
+        dtype=torch.int64,
+        device=logcnt.device,
+    )
+    log2phy.view(num_layers, -1).scatter_(
+        -1,
+        phy2log * maxlogcnt + phyrank,
+        torch.arange(
+            num_local_experts * num_active_ranks,
+            dtype=torch.int64,
+            device=log2phy.device,
+        ).expand(num_layers, -1),
+    )
+    if num_active_ranks < num_gpus:
+        phy2log_slices = list(
+            phy2log.view(num_layers, num_active_ranks, -1).unbind(dim=1)
+        )
+        active_ranks_list = active_ranks.tolist()
+        for idx, active_rank in enumerate(active_ranks_list):
+            if not active_rank:
+                phy2log_slices.insert(idx, torch.zeros_like(phy2log_slices[0]))
+                log2phy = torch.where(
+                    log2phy >= idx * num_local_experts,
+                    log2phy + num_local_experts,
+                    log2phy,
+                )
+        phy2log = torch.stack(phy2log_slices, dim=1).contiguous().view(num_layers, -1)
+    return phy2log, log2phy, logcnt

python/sglang/srt/layers/moe/token_dispatcher/mooncake.py

@@ -4,6 +4,7 @@
 from dataclasses import dataclass
 from typing import NamedTuple, Optional, Tuple
 
+from sglang.srt.elastic_ep.elastic_ep import ElasticEPStateManager
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.layers.dp_attention import get_is_extend_in_batch
 from sglang.srt.layers.moe.token_dispatcher.base import (
@@ -63,14 +64,6 @@ def format(self) -> CombineInputFormat:
 assert isinstance(MooncakeCombineInput, CombineInput)
 
 
-_ACTIVE_RANKS: Optional[torch.Tensor] = None
-
-
-def get_ep_active_ranks() -> torch.Tensor:
-    assert _ACTIVE_RANKS is not None, "_ACTIVE_RANKS is not initialized"
-    return _ACTIVE_RANKS
-
-
 class EPBuffer:
     _buffer = None
     _hidden_size: Optional[int] = None
@@ -153,12 +146,7 @@ def __init__(
         self.first_execution = True
         self.timeout_us = 10000000
 
-        global _ACTIVE_RANKS
-        if _ACTIVE_RANKS is None:
-            _ACTIVE_RANKS = torch.ones(
-                (self.num_experts,), dtype=torch.int32, device="cuda"
-            )
-        self.active_ranks = _ACTIVE_RANKS
+        self.active_ranks = ElasticEPStateManager.instance().active_ranks
 
         self.handle = None
 

python/sglang/srt/model_executor/model_runner.py

@@ -24,7 +24,7 @@
 import time
 from collections import defaultdict
 from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
+from typing import Callable, List, Optional, Tuple, Union
 
 import torch
 import torch.distributed as dist
@@ -51,6 +51,7 @@
     set_symm_mem_all_reduce,
 )
 from sglang.srt.distributed.parallel_state import monkey_patch_vllm_parallel_state
+from sglang.srt.elastic_ep.elastic_ep import ElasticEPStateManager
 from sglang.srt.eplb.eplb_manager import EPLBManager
 from sglang.srt.eplb.expert_distribution import (
     ExpertDistributionRecorder,
@@ -379,6 +380,11 @@ def initialize(self, min_per_gpu_memory: float):
         )
         self.expert_location_updater = ExpertLocationUpdater()
 
+        (
+            ElasticEPStateManager.init(self.server_args)
+            if self.server_args.elastic_ep_backend
+            else None
+        )
         # Load the model
         self.sampler = Sampler()
         self.load_model()
@@ -945,16 +951,33 @@ def update_expert_location(
         new_expert_location_metadata: ExpertLocationMetadata,
         update_layer_ids: List[int],
     ):
-        self.expert_location_updater.update(
-            self.model.routed_experts_weights_of_layer,
-            new_expert_location_metadata,
-            update_layer_ids=update_layer_ids,
-            nnodes=self.server_args.nnodes,
-            rank=self.tp_rank,
-        )
+        if ElasticEPStateManager.instance() is not None:
+            # TODO: refactor the weights update when elastic ep
+            old_expert_location_metadata = get_global_expert_location_metadata()
+            assert old_expert_location_metadata is not None
+            old_expert_location_metadata.update(
+                new_expert_location_metadata,
+                update_layer_ids=update_layer_ids,
+            )
+            self.update_weights_from_disk(
+                self.server_args.model_path,
+                self.server_args.load_format,
+                lambda name: "mlp.experts" in name and "mlp.shared_experts" not in name,
+            )
+        else:
+            self.expert_location_updater.update(
+                self.model.routed_experts_weights_of_layer,
+                new_expert_location_metadata,
+                update_layer_ids=update_layer_ids,
+                nnodes=self.server_args.nnodes,
+                rank=self.tp_rank,
+            )
 
     def update_weights_from_disk(
-        self, model_path: str, load_format: str
+        self,
+        model_path: str,
+        load_format: str,
+        weight_name_filter: Optional[Callable[[str], bool]] = None,
     ) -> tuple[bool, str]:
         """Update engine weights in-place from the disk."""
         logger.info(
@@ -976,6 +999,11 @@ def get_weight_iter(config):
             iter = loader._get_weights_iterator(
                 DefaultModelLoader.Source.init_new(config, self.model)
             )
+            if weight_name_filter is not None:
+                iter = (
+                    (name, weight) for name, weight in iter if weight_name_filter(name)
+                )
+
             return iter
 
         def model_load_weights(model, iter):

python/sglang/srt/server_args.py

@@ -597,6 +597,9 @@ def __post_init__(self):
         # Handle any other necessary validations.
         self._handle_other_validations()
 
+        # Handle elastic expert parallelism.
+        self._handle_elastic_ep()
+
     def _handle_deprecated_args(self):
         # handle deprecated tool call parsers
         deprecated_tool_call_parsers = {"qwen25": "qwen", "glm45": "glm"}
@@ -1222,6 +1225,15 @@ def _handle_eplb_and_dispatch(self):
         if self.enable_eplb:
             assert self.ep_size > 1
 
+    def _handle_elastic_ep(self):
+        if self.elastic_ep_backend is not None:
+            if self.enable_eplb:
+                if self.eplb_algorithm == "auto":
+                    self.eplb_algorithm = "elasticity_aware"
+                assert (
+                    self.eplb_algorithm == "elasticity_aware"
+                ), "Elastic EP requires eplb_algorithm to be set to 'auto' or 'elasticity_aware'."
+
     def _handle_expert_distribution_metrics(self):
         if self.enable_expert_distribution_metrics and (
             self.expert_distribution_recorder_mode is None