mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-01 14:52:33 +08:00
223 lines
8.2 KiB
Python
223 lines
8.2 KiB
Python
"""
|
|
This file is copied from https://github.com/deepseek-ai/EPLB/blob/main/eplb.py
|
|
"""
|
|
|
|
"""Expert Parallelism Load Balancer (EPLB)"""
|
|
|
|
from typing import Tuple
|
|
|
|
import numpy as np
|
|
|
|
|
|
def balanced_packing(weight: np.ndarray, num_packs: int) -> Tuple[np.ndarray, np.ndarray]:
|
|
"""
|
|
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 = np.arange(weight.shape[-1], dtype=np.int32).reshape(1, -1).repeat(num_layers, axis=0)
|
|
rank_in_pack = np.zeros_like(weight, dtype=np.int32)
|
|
return pack_index, rank_in_pack
|
|
|
|
indices = np.argsort(-weight.astype(np.float32), axis=-1)
|
|
pack_index = np.full_like(weight, fill_value=-1, dtype=np.int32)
|
|
rank_in_pack = np.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: np.ndarray, num_phy: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
"""
|
|
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
|
|
phy2log = np.arange(num_phy, dtype=np.int32).reshape(1, -1).repeat(n, axis=0)
|
|
rank = np.zeros((n, num_phy), dtype=np.int32)
|
|
logcnt = np.ones((n, num_log), dtype=np.int32)
|
|
arangen = np.arange(n, dtype=np.int32)
|
|
for i in range(num_log, num_phy):
|
|
redundant_indices = np.argmax(weight / logcnt, axis=-1)
|
|
phy2log[:, i] = redundant_indices
|
|
rank[:, i] = logcnt[arangen, redundant_indices]
|
|
logcnt[arangen, redundant_indices] += 1
|
|
return phy2log, rank, logcnt
|
|
|
|
|
|
def rebalance_experts_hierarchical(
|
|
weight: np.ndarray,
|
|
num_physical_experts: int,
|
|
num_groups: int,
|
|
num_nodes: int,
|
|
num_gpus: int,
|
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
"""
|
|
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: np.ndarray) -> np.ndarray:
|
|
inv = np.empty_like(perm)
|
|
inv[np.arange(perm.shape[0])[:, None], perm] = np.arange(perm.shape[1], dtype=np.int32).reshape(1, -1)
|
|
return inv
|
|
|
|
# Step 1: pack groups to nodes
|
|
tokens_per_group = weight.reshape(num_layers, num_groups, group_size).sum(axis=-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)[:, :, None]
|
|
+ np.arange(group_size, dtype=np.int32)
|
|
).reshape(num_layers, -1)
|
|
mlog2log = inverse(log2mlog)
|
|
|
|
# Step 2: construct redundant experts within nodes
|
|
tokens_per_mlog = np.take_along_axis(weight, mlog2log, axis=-1).reshape(-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
|
|
tokens_per_phy = np.take_along_axis(tokens_per_mlog / mlogcnt, phy2mlog, axis=-1)
|
|
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 = np.take_along_axis(phy2mlog, pphy2phy, axis=-1) # [num_layers * num_nodes, num_log_per_nodes]
|
|
pphy2mlog = (
|
|
pphy2mlog.reshape(num_layers, num_nodes, -1)
|
|
+ np.arange(
|
|
0,
|
|
num_logical_experts,
|
|
num_logical_experts // num_nodes,
|
|
dtype=np.int32,
|
|
).reshape(1, -1, 1)
|
|
).reshape(num_layers, -1)
|
|
pphy2log = np.take_along_axis(mlog2log, pphy2mlog, axis=-1)
|
|
pphyrank = np.take_along_axis(phyrank, pphy2phy, axis=-1).reshape(num_layers, -1)
|
|
logcnt = np.take_along_axis(mlogcnt.reshape(num_layers, -1), log2mlog, axis=-1)
|
|
return pphy2log, pphyrank, logcnt
|
|
|
|
|
|
def rebalance_experts(
|
|
weight: np.ndarray,
|
|
num_replicas: int,
|
|
num_groups: int,
|
|
num_nodes: int,
|
|
num_gpus: int,
|
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
"""
|
|
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.astype(np.float32)
|
|
if num_groups % num_nodes == 0:
|
|
# 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 = replicate_experts(weight, num_replicas)
|
|
maxlogcnt = logcnt.max()
|
|
log2phy = np.full((num_layers, num_logical_experts, maxlogcnt), -1, dtype=np.int32)
|
|
np.put_along_axis(
|
|
log2phy.reshape(num_layers, -1)[:, :, None],
|
|
(phy2log * maxlogcnt + phyrank)[:, :, None],
|
|
np.arange(num_replicas, dtype=np.int32).reshape(1, -1).repeat(num_layers, axis=0)[:, :, None],
|
|
axis=1,
|
|
)
|
|
return phy2log, log2phy, logcnt
|
|
|
|
|
|
__all__ = ["rebalance_experts"]
|
|
|
|
|
|
def main():
|
|
""" """
|
|
num_hidden_layers = 3
|
|
num_expert = 64
|
|
num_groups = 8
|
|
|
|
num_replicas = 64
|
|
num_nodes = 4
|
|
num_gpus = 4 * 8
|
|
|
|
model_tokens_per_expert_stats_list = np.random.randint(low=1, high=10, size=(num_hidden_layers, num_expert))
|
|
|
|
phy2log, phyrank, logcnt = rebalance_experts(
|
|
model_tokens_per_expert_stats_list,
|
|
num_replicas,
|
|
num_groups,
|
|
num_nodes,
|
|
num_gpus,
|
|
)
|
|
|
|
print(phy2log)
|
|
print(phyrank)
|
|
print(logcnt)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|