Ilya MarkovandTyler Michael Smith
6170d47d22
Signed-off-by: ilmarkov <markovilya197@gmail.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
380 lines
16 KiB
Python
380 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Expert parallelism load balancer (EPLB) for vLLM.
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This module implements the core rearrangement algorithm.
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The rearrangement algorithm is adapted from
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[DeepSeek EPLB](https://github.com/deepseek-ai/eplb).
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Please find at [#12](https://github.com/deepseek-ai/EPLB/issues/12) an example
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on how the EPLB algorithm works.
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"""
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import numpy as np
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import torch
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from .abstract import AbstractEplbPolicy
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class DefaultEplbPolicy(AbstractEplbPolicy):
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@classmethod
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def balanced_packing(
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cls, weight: torch.Tensor, num_packs: int
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Pack n weighted objects to m packs, such that each bin contains exactly
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n/m objects and the weights of all packs are as balanced as possible.
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Parameters:
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weight: [X, n], the weight of each item
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num_packs: number of packs
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Returns:
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pack_index: [X, n], the pack index of each item
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rank_in_pack: [X, n], the rank of the item in the pack
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"""
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num_layers, num_groups = weight.shape
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assert num_groups % num_packs == 0
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groups_per_pack = num_groups // num_packs
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device = weight.device
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if groups_per_pack == 1:
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pack_index = torch.arange(
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weight.size(-1), dtype=torch.int64, device=device
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).expand(weight.shape)
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rank_in_pack = torch.zeros_like(weight, dtype=torch.int64, device=device)
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return pack_index, rank_in_pack
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weight_np = weight.cpu().numpy()
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# Sort and get indices in decending order
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indices_np = np.argsort(-weight_np, axis=-1)
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pack_index_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
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rank_in_pack_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
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# Run the packing algorithm
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for i in range(num_layers):
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pack_weights = [0.0] * num_packs
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pack_items = [0] * num_packs
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for group in indices_np[i]:
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# Find a pack with capacity that has the lowest weight
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pack = min(
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(j for j in range(num_packs) if pack_items[j] < groups_per_pack),
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key=pack_weights.__getitem__,
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)
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assert pack_items[pack] < groups_per_pack
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pack_index_np[i, group] = pack
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rank_in_pack_np[i, group] = pack_items[pack]
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pack_weights[pack] += weight_np[i, group]
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pack_items[pack] += 1
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pack_index = torch.from_numpy(pack_index_np).to(device)
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rank_in_pack = torch.from_numpy(rank_in_pack_np).to(device)
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return pack_index, rank_in_pack
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@classmethod
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def replicate_experts(
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cls, weight: torch.Tensor, num_phy: int
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Replicate `num_log` experts to `num_phy` replicas, such that the maximum
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load of all replicas is minimized.
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Parameters:
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weight: [X, num_log]
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num_phy: total number of experts after replication
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Returns:
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phy2log: [X, num_phy], logical expert id of each physical expert
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replica_idx: [X, num_phy], the index of the replica for each logical expert
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logcnt: [X, num_log], number of replicas for each logical expert
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"""
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n, num_log = weight.shape
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num_redundant = num_phy - num_log
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assert num_redundant >= 0
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device = weight.device
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phy2log = torch.arange(num_phy, dtype=torch.int64, device=device).repeat(n, 1)
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replica_idx = torch.zeros(n, num_phy, dtype=torch.int64, device=device)
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logcnt = torch.ones(n, num_log, dtype=torch.int64, device=device)
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arangen = torch.arange(n, dtype=torch.int64, device=device)
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for i in range(num_log, num_phy):
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redundant_indices = (weight / logcnt).max(dim=-1).indices
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phy2log[:, i] = redundant_indices
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replica_idx[:, i] = logcnt[arangen, redundant_indices]
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logcnt[arangen, redundant_indices] += 1
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return phy2log, replica_idx, logcnt
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@classmethod
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def rebalance_experts_hierarchical(
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cls,
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weight: torch.Tensor,
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num_physical_experts: int,
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num_groups: int,
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num_nodes: int,
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num_gpus: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Parameters:
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weight: [num_moe_layers, num_logical_experts]
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num_physical_experts: number of physical experts after replication
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num_groups: number of expert groups
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num_nodes: number of server nodes, where the intra-node network
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(e.g, NVLink) is faster
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num_gpus: number of GPUs, must be a multiple of `num_nodes`
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Returns:
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phy2log: [layers, num_replicas], the expert
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index of each replica
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pphy_replicas_idx: [layers, num_logical_experts, X],
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the replica indices for each expert
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logcnt: [layers, num_logical_experts], number of
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physical replicas for each logical expert
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"""
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num_layers, num_logical_experts = weight.shape
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assert num_logical_experts % num_groups == 0
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group_size = num_logical_experts // num_groups
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assert num_groups % num_nodes == 0
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groups_per_node = num_groups // num_nodes
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assert num_gpus % num_nodes == 0
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assert num_physical_experts % num_gpus == 0
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phy_experts_per_gpu = num_physical_experts // num_gpus
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def inverse(perm: torch.Tensor) -> torch.Tensor:
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inv = torch.empty_like(perm)
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inv.scatter_(
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1,
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perm,
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torch.arange(
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perm.size(1), dtype=torch.int64, device=perm.device
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).expand(perm.shape),
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)
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return inv
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# Step 1: pack groups to nodes
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tokens_per_group = weight.unflatten(-1, (num_groups, group_size)).sum(-1)
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group_pack_index, group_rank_in_pack = cls.balanced_packing(
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tokens_per_group, num_nodes
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)
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log2mlog = (
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(
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(group_pack_index * groups_per_node + group_rank_in_pack) * group_size
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).unsqueeze(-1)
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+ torch.arange(
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group_size, dtype=torch.int64, device=group_pack_index.device
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)
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).flatten(-2)
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mlog2log = inverse(log2mlog)
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# Step 2: construct redundant experts within nodes
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# [num_layers * num_nodes, num_logical_experts // num_nodes]
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tokens_per_mlog = weight.gather(-1, mlog2log).view(
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-1, num_logical_experts // num_nodes
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)
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phy2mlog, replicas_idx, mlogcnt = cls.replicate_experts(
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tokens_per_mlog, num_physical_experts // num_nodes
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)
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# Step 3: pack physical_experts to GPUs
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# [num_layers * num_nodes, num_physical_experts // num_nodes]
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tokens_per_phy = (tokens_per_mlog / mlogcnt).gather(-1, phy2mlog)
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pack_index, rank_in_pack = cls.balanced_packing(
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tokens_per_phy, num_gpus // num_nodes
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)
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phy2pphy = pack_index * phy_experts_per_gpu + rank_in_pack
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pphy2phy = inverse(phy2pphy)
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pphy2mlog = phy2mlog.gather(
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-1, pphy2phy
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) # [num_layers * num_nodes, num_log_per_nodes]
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pphy2mlog = (
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pphy2mlog.view(num_layers, num_nodes, -1)
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+ torch.arange(
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0,
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num_logical_experts,
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num_logical_experts // num_nodes,
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device=group_pack_index.device,
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).view(1, -1, 1)
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).flatten(-2)
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pphy2log = mlog2log.gather(-1, pphy2mlog)
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pphy_replicas_idx = replicas_idx.gather(-1, pphy2phy).view(num_layers, -1)
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logcnt = mlogcnt.view(num_layers, -1).gather(-1, log2mlog)
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return pphy2log, pphy_replicas_idx, logcnt
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@classmethod
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def preserve_intragpu_slots(
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cls,
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phy2log: torch.Tensor,
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phy_replicas_idx: torch.Tensor,
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num_ranks: int,
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old_global_expert_indices: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Reorder the new mapping per GPU so that experts that remain on the same GPU
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keep their previous slot positions when possible. Incoming experts to that GPU
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fill any remaining available slots. This is applied only when the number of GPUs
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is unchanged and the slots per GPU remain the same between
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the old and new mappings.
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"""
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device = phy2log.device
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num_phy_experts = phy2log.shape[1]
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if num_ranks <= 0 or num_phy_experts % num_ranks != 0:
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return phy2log, phy_replicas_idx
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# Move to CPU and convert to NumPy for processing
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new_phy2log_np = phy2log.cpu().numpy()
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replicas_idx_np = phy_replicas_idx.cpu().numpy()
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old_phy2log_np = old_global_expert_indices.cpu().numpy()
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slots_per_gpu = num_phy_experts // num_ranks
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num_layers = new_phy2log_np.shape[0]
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post_phy2log_np = new_phy2log_np.copy()
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post_phy_replicas_idx_np = replicas_idx_np.copy()
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for gpu_idx in range(num_ranks):
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start = gpu_idx * slots_per_gpu
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end = start + slots_per_gpu
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# Experts across all layers for this GPU
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old_local = old_phy2log_np[:, start:end] # [layers, slots]
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new_local = new_phy2log_np[:, start:end] # [layers, slots]
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new_ridx = replicas_idx_np[:, start:end] # [layers, slots]
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used_new_indices = np.zeros((num_layers, slots_per_gpu), dtype=bool)
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preserved_positions = np.zeros((num_layers, slots_per_gpu), dtype=bool)
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# First pass: preserve same-logical experts in their previous slots
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for slot_idx in range(slots_per_gpu):
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# matches: [layers, slots], True where new local experts have
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# the same logical value as the old from 'slot_idx' and not checked yet
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matches = (new_local == old_local[:, slot_idx][:, None]) & (
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~used_new_indices
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)
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has_any = matches.any(axis=1)
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if np.any(has_any):
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first_idx = np.argmax(matches, axis=1)
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layer_indices = np.nonzero(has_any)[0]
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matched_new_positions = first_idx[layer_indices]
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post_phy2log_np[layer_indices, start + slot_idx] = new_local[
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layer_indices, matched_new_positions
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]
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post_phy_replicas_idx_np[layer_indices, start + slot_idx] = (
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new_ridx[layer_indices, matched_new_positions]
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)
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used_new_indices[layer_indices, matched_new_positions] = True
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preserved_positions[layer_indices, slot_idx] = True
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# Second pass: fill remaining slots with remaining new experts
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remaining_mask = ~used_new_indices # [layers, slots]
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fill_mask = ~preserved_positions # [layers, slots]
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if remaining_mask.any() and fill_mask.any():
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idx_base = np.tile(np.arange(slots_per_gpu), (num_layers, 1))
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# Sentinel value for unavailable positions.
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large = slots_per_gpu + 1
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# Priorities: keep original index for available spots, set sentinel
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# for unavailable; lower is earlier.
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remaining_priority = np.where(remaining_mask, idx_base, large)
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fill_priority = np.where(fill_mask, idx_base, large)
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# Sort to get ordered indices of available src/dst positions per layer.
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remaining_indices = np.argsort(remaining_priority, axis=1)
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fill_indices = np.argsort(fill_priority, axis=1)
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# Fill count per layer (cannot exceed either side).
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remaining_counts = remaining_mask.sum(axis=1)
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fill_counts = fill_mask.sum(axis=1)
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take_counts = np.minimum(remaining_counts, fill_counts)
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# Assign remaining new experts to remaining slots per layer.
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for layer_idx in range(num_layers):
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k = int(take_counts[layer_idx])
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if k <= 0:
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continue
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src_pos = remaining_indices[layer_idx, :k]
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dst_pos = fill_indices[layer_idx, :k]
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post_phy2log_np[layer_idx, start + dst_pos] = new_local[
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layer_idx, src_pos
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]
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post_phy_replicas_idx_np[layer_idx, start + dst_pos] = new_ridx[
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layer_idx, src_pos
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]
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# Convert back to torch and move to original device
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post_phy2log = torch.from_numpy(post_phy2log_np).to(device)
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post_phy_replicas_idx = torch.from_numpy(post_phy_replicas_idx_np).to(device)
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return post_phy2log, post_phy_replicas_idx
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@classmethod
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def rebalance_experts(
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cls,
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weight: torch.Tensor,
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num_replicas: int,
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num_groups: int,
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num_nodes: int,
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num_ranks: int,
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old_global_expert_indices: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Entry point for expert-parallelism load balancer.
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Parameters:
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weight: [layers, num_logical_experts], the load statistics for all
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logical experts
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num_replicas: number of physical experts, must be a multiple of
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`num_gpus`
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num_groups: number of expert groups
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num_nodes: number of server nodes, where the intra-node network
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(e.g, NVLink) is faster
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num_ranks: number of ranks, must be a multiple of `num_nodes`
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old_global_expert_indices: [layers, num_logical_experts], the old global
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expert indices. Used to avoid unnecessary weight copying
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for experts moving within one rank.
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Returns:
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phy2log: [layers, num_replicas], the expert
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index of each replica
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log2phy: [layers, num_logical_experts, X],
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the replica indices for each expert
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logcnt: [layers, num_logical_experts], number of
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physical replicas for each logical expert
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"""
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num_layers, num_logical_experts = weight.shape
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weight = weight.float()
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if num_groups % num_nodes == 0:
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# use hierarchical load-balance policy
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phy2log, phy_replicas_idx, logcnt = cls.rebalance_experts_hierarchical(
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weight, num_replicas, num_groups, num_nodes, num_ranks
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)
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else:
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# use global load-balance policy
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phy2log, phy_replicas_idx, logcnt = cls.rebalance_experts_hierarchical(
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weight, num_replicas, 1, 1, num_ranks
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)
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# Optional postprocessing to preserve slots for experts moving
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# within the same GPU
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# Only apply when the number of GPUs and slots per GPU remain unchanged.
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# Helps to avoid unnecessary weight copying when experts move
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# within the same GPU.
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if old_global_expert_indices is not None:
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phy2log, phy_replicas_idx = cls.preserve_intragpu_slots(
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phy2log, phy_replicas_idx, num_ranks, old_global_expert_indices
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)
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num_redundant_experts = num_replicas - num_logical_experts
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maxlogcnt = num_redundant_experts + 1
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log2phy: torch.Tensor = torch.full(
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(num_layers, num_logical_experts, maxlogcnt),
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-1,
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dtype=torch.int64,
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device=logcnt.device,
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)
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log2phy.view(num_layers, -1).scatter_(
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-1,
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phy2log * maxlogcnt + phy_replicas_idx,
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torch.arange(num_replicas, dtype=torch.int64, device=log2phy.device).expand(
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num_layers, -1
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),
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)
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return phy2log, log2phy, logcnt
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