Support picking variants of EPLB algorithms (#6728)

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

+from enum import Enum, auto
+from typing import Optional
+
+import torch
+
+from sglang.srt.managers.eplb_algorithms import deepseek, deepseek_vec
+
+
+class EplbAlgorithm(Enum):
+    deepseek = auto()
+    deepseek_hierarchical = auto()
+    deepseek_vec = auto()
+    deepseek_vec_hierarchical = auto()
+    # TODO may have more algorithm later
+
+
+def rebalance_experts(
+    tokens_per_expert: torch.Tensor,
+    num_physical_experts: int,
+    num_local_physical_experts: int,
+    num_groups: Optional[int],
+    num_nodes: int,
+    algorithm: EplbAlgorithm,
+):
+    if algorithm in [EplbAlgorithm.deepseek, EplbAlgorithm.deepseek_hierarchical]:
+        return deepseek.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=algorithm == EplbAlgorithm.deepseek_hierarchical,
+        )
+
+    if algorithm in [
+        EplbAlgorithm.deepseek_vec,
+        EplbAlgorithm.deepseek_vec_hierarchical,
+    ]:
+        return deepseek_vec.rebalance_experts(
+            tokens_per_expert=tokens_per_expert,
+            num_physical_experts=num_physical_experts,
+            num_local_physical_experts=num_local_physical_experts,
+            num_groups=num_groups,
+            num_nodes=num_nodes,
+            enable_hierarchical=algorithm == EplbAlgorithm.deepseek_vec_hierarchical,
+        )
+
+    raise NotImplementedError
+
+
+def compute_algorithm(
+    raw_algorithm: str,
+    num_groups: Optional[int],
+    num_nodes: int,
+) -> EplbAlgorithm:
+    if raw_algorithm != "auto":
+        return EplbAlgorithm[raw_algorithm]
+
+    # TODO test on real scenarios and know which ones perform better
+    if (num_groups is not None) and (num_groups % num_nodes == 0):
+        return EplbAlgorithm.deepseek_hierarchical
+    else:
+        return EplbAlgorithm.deepseek

python/sglang/srt/managers/eplb_algorithms/deepseek.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 Tuple
+
+import torch
+
+from sglang.srt.utils import get_bool_env_var
+
+
+def balanced_packing(
+    weight: torch.Tensor, num_packs: int
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Pack n weighted objects to m packs, such that each bin contains exactly n/m objects and the weights of all packs
+    are as balanced as possible.
+
+    Parameters:
+        weight: [X, n], the weight of each item
+        num_packs: number of packs
+
+    Returns:
+        pack_index: [X, n], the pack index of each item
+        rank_in_pack: [X, n], the rank of the item in the pack
+    """
+    num_layers, num_groups = weight.shape
+    assert num_groups % num_packs == 0
+    groups_per_pack = num_groups // num_packs
+
+    if groups_per_pack == 1:
+        pack_index = torch.arange(
+            weight.size(-1), dtype=torch.int64, device=weight.device
+        ).expand(weight.shape)
+        rank_in_pack = torch.zeros_like(weight, dtype=torch.int64)
+        return pack_index, rank_in_pack
+
+    indices = weight.float().sort(-1, descending=True).indices.cpu()
+    pack_index = torch.full_like(weight, fill_value=-1, dtype=torch.int64, device="cpu")
+    rank_in_pack = torch.full_like(pack_index, fill_value=-1)
+    for i in range(num_layers):
+        pack_weights = [0] * num_packs
+        pack_items = [0] * num_packs
+        for group in indices[i]:
+            pack = min(
+                (i for i in range(num_packs) if pack_items[i] < groups_per_pack),
+                key=pack_weights.__getitem__,
+            )
+            assert pack_items[pack] < groups_per_pack
+            pack_index[i, group] = pack
+            rank_in_pack[i, group] = pack_items[pack]
+            pack_weights[pack] += weight[i, group]
+            pack_items[pack] += 1
+    return pack_index, rank_in_pack
+
+
+def replicate_experts(
+    weight: torch.Tensor, num_phy: int
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Replicate `num_log` experts to `num_phy` replicas, such that the maximum load of all replicas is minimized.
+
+    Parameters:
+        weight: [X, num_log]
+        num_phy: total number of experts after replication
+
+    Returns:
+        phy2log: [X, num_phy], logical expert id of each physical expert
+        rank: [X, num_phy], the replica rank
+        logcnt: [X, num_log], number of replicas for each logical expert
+    """
+    n, num_log = weight.shape
+    num_redundant = num_phy - num_log
+    assert num_redundant >= 0
+    device = weight.device
+    phy2log = torch.arange(num_phy, dtype=torch.int64, device=device).repeat(n, 1)
+    rank = torch.zeros(n, num_phy, dtype=torch.int64, device=device)
+    logcnt = torch.ones(n, num_log, dtype=torch.int64, device=device)
+    arangen = torch.arange(n, dtype=torch.int64, device=device)
+    for i in range(num_log, num_phy):
+        redundant_indices = (weight / logcnt).max(dim=-1).indices
+        phy2log[:, i] = redundant_indices
+        rank[:, i] = logcnt[arangen, redundant_indices]
+        logcnt[arangen, redundant_indices] += 1
+    return phy2log, rank, logcnt
+
+
+def rebalance_experts_hierarchical(
+    weight: torch.Tensor,
+    num_physical_experts: int,
+    num_groups: int,
+    num_nodes: int,
+    num_gpus: int,
+):
+    """
+    Parameters:
+        weight: [num_moe_layers, num_logical_experts]
+        num_physical_experts: number of physical experts after replication
+        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: [num_moe_layers, num_physical_experts]
+        logical_to_physical_map: [num_moe_layers, num_logical_experts, X]
+        logical_count: [num_moe_layers, num_logical_experts]
+    """
+    num_layers, num_logical_experts = weight.shape
+    assert num_logical_experts % num_groups == 0
+    group_size = num_logical_experts // num_groups
+    assert num_groups % num_nodes == 0
+    groups_per_node = num_groups // num_nodes
+    assert num_gpus % num_nodes == 0
+    assert num_physical_experts % num_gpus == 0
+    phy_experts_per_gpu = num_physical_experts // num_gpus
+
+    def inverse(perm: torch.Tensor) -> torch.Tensor:
+        inv = torch.empty_like(perm)
+        inv.scatter_(
+            1,
+            perm,
+            torch.arange(perm.size(1), dtype=torch.int64, device=perm.device).expand(
+                perm.shape
+            ),
+        )
+        return inv
+
+    # Step 1: pack groups to nodes
+    tokens_per_group = weight.unflatten(-1, (num_groups, group_size)).sum(-1)
+    group_pack_index, group_rank_in_pack = balanced_packing(tokens_per_group, num_nodes)
+    log2mlog = (
+        (
+            (group_pack_index * groups_per_node + group_rank_in_pack) * group_size
+        ).unsqueeze(-1)
+        + torch.arange(group_size, dtype=torch.int64, device=group_pack_index.device)
+    ).flatten(-2)
+    mlog2log = inverse(log2mlog)
+
+    # Step 2: construct redundant experts within nodes
+    # [num_layers * num_nodes, num_logical_experts // num_nodes]
+    tokens_per_mlog = weight.gather(-1, mlog2log).view(
+        -1, num_logical_experts // num_nodes
+    )
+    phy2mlog, phyrank, mlogcnt = replicate_experts(
+        tokens_per_mlog, num_physical_experts // num_nodes
+    )
+
+    # Step 3: pack physical_experts to GPUs
+    # [num_layers * num_nodes, num_physical_experts // num_nodes]
+    tokens_per_phy = (tokens_per_mlog / mlogcnt).gather(-1, phy2mlog)
+    pack_index, rank_in_pack = balanced_packing(tokens_per_phy, num_gpus // num_nodes)
+    phy2pphy = pack_index * phy_experts_per_gpu + rank_in_pack
+    pphy2phy = inverse(phy2pphy)
+
+    pphy2mlog = phy2mlog.gather(
+        -1, pphy2phy
+    )  # [num_layers * num_nodes, num_log_per_nodes]
+    pphy2mlog = (
+        pphy2mlog.view(num_layers, num_nodes, -1)
+        + torch.arange(
+            0,
+            num_logical_experts,
+            num_logical_experts // num_nodes,
+            device=group_pack_index.device,
+        ).view(1, -1, 1)
+    ).flatten(-2)
+    pphy2log = mlog2log.gather(-1, pphy2mlog)
+    pphyrank = phyrank.gather(-1, pphy2phy).view(num_layers, -1)
+    logcnt = mlogcnt.view(num_layers, -1).gather(-1, log2mlog)
+    return pphy2log, pphyrank, logcnt
+
+
+def rebalance_experts(
+    weight: torch.Tensor,
+    num_replicas: int,
+    num_groups: int,
+    num_nodes: int,
+    num_gpus: int,
+    enable_hierarchical: bool,
+) -> 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()
+    if 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_replicas, dtype=torch.int64, device=log2phy.device).expand(
+            num_layers, -1
+        ),
+    )
+    return phy2log, log2phy, logcnt
+
+
+__all__ = ["rebalance_experts"]

python/sglang/srt/managers/deepseek_eplb.py → python/sglang/srt/managers/eplb_algorithms/deepseek_vec.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, Optional, Tuple
+from typing import Optional, Tuple
 
 import torch
 
@@ -259,13 +258,9 @@ def rebalance_experts(
     num_local_physical_experts: int,
     num_groups: Optional[int],
     num_nodes: int,
-    phase: Literal["prefill", "decode", "null"],
+    enable_hierarchical: bool,
 ):
-    if (
-        (phase == "prefill")
-        and (num_groups is not None)
-        and (num_groups % num_nodes == 0)
-    ):
+    if enable_hierarchical:
         return prefill_rebalance_experts(
             tokens_per_expert=tokens_per_expert,
             num_physical_experts=num_physical_experts,
@@ -273,8 +268,9 @@ def rebalance_experts(
             num_groups=num_groups,
             num_nodes=num_nodes,
         )
-    return decode_rebalance_experts(
-        tokens_per_expert=tokens_per_expert,
-        num_physical_experts=num_physical_experts,
-        num_local_physical_experts=num_local_physical_experts,
-    )
+    else:
+        return decode_rebalance_experts(
+            tokens_per_expert=tokens_per_expert,
+            num_physical_experts=num_physical_experts,
+            num_local_physical_experts=num_local_physical_experts,
+        )

python/sglang/srt/managers/expert_location.py

@@ -22,7 +22,7 @@ import torch.distributed
 import torch.nn.functional as F
 
 from sglang.srt.configs.model_config import ModelConfig
-from sglang.srt.managers import deepseek_eplb
+from sglang.srt.managers import eplb_algorithms
 from sglang.srt.model_loader import get_model_architecture
 from sglang.srt.server_args import ServerArgs
 
@@ -134,15 +134,21 @@ class ExpertLocationMetadata:
         common = ExpertLocationMetadata._init_common(server_args, model_config)
         model_config_for_expert_location = common["model_config_for_expert_location"]
         num_physical_experts = common["num_physical_experts"]
+        num_groups = model_config_for_expert_location.num_groups
+        num_nodes = server_args.nnodes
 
         physical_to_logical_map, logical_to_all_physical_map, expert_count = (
-            deepseek_eplb.rebalance_experts(
+            eplb_algorithms.rebalance_experts(
                 tokens_per_expert=logical_count,
                 num_physical_experts=num_physical_experts,
                 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=server_args.disaggregation_mode,
+                num_groups=num_groups,
+                num_nodes=num_nodes,
+                algorithm=eplb_algorithms.compute_algorithm(
+                    raw_algorithm=server_args.eplb_algorithm,
+                    num_groups=num_groups,
+                    num_nodes=num_nodes,
+                ),
             )
         )
 

python/sglang/srt/server_args.py

@@ -175,6 +175,7 @@ class ServerArgs:
     ep_dispatch_algorithm: Optional[Literal["static", "dynamic", "fake"]] = None
     init_expert_location: str = "trivial"
     enable_eplb: bool = False
+    eplb_algorithm: str = "auto"
     eplb_rebalance_num_iterations: int = 1000
     expert_distribution_recorder_mode: Optional[
         Literal["stat", "per_pass", "per_token"]
@@ -1328,6 +1329,12 @@ class ServerArgs:
             action="store_true",
             help="Enable EPLB algorithm",
         )
+        parser.add_argument(
+            "--eplb-algorithm",
+            type=str,
+            default=ServerArgs.eplb_algorithm,
+            help="Chosen EPLB algorithm",
+        )
         parser.add_argument(
             "--eplb-rebalance-num-iterations",
             type=int,