PyTorch implementation of Soft Mixture of Experts (Soft-MoE) from "From Sparse to Soft Mixtures of Experts".
This implementation extends the timm
library's VisionTransformer
class to support Soft-MoE MLP layers.
pip install soft-moe
Or install the entire repo with:
git clone https://github.com/bwconrad/soft-moe
cd soft-moe/
pip install -r requirements.txt
import torch
from soft_moe import SoftMoEVisionTransformer
net = SoftMoEVisionTransformer(
num_experts=128,
slots_per_expert=1,
moe_layer_index=6,
img_size=224,
patch_size=32,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
)
img = torch.randn(1, 3, 224, 224)
preds = net(img)
Functions are also available to initialize default network configurations:
from soft_moe import (soft_moe_vit_base, soft_moe_vit_huge,
soft_moe_vit_large, soft_moe_vit_small,
soft_moe_vit_tiny)
net = soft_moe_vit_tiny()
net = soft_moe_vit_small()
net = soft_moe_vit_base()
net = soft_moe_vit_large()
net = soft_moe_vit_huge()
net = soft_moe_vit_tiny(num_experts=64, slots_per_expert=2, img_size=128)
The moe_layer_index
argument sets at which layer indices to use MoE MLP layers instead of regular MLP layers.
When an int
is given, all layers starting from that depth index will be MoE layers.
net = SoftMoEVisionTransformer(
moe_layer_index=6, # Blocks 6-12
depth=12,
)
When a list
is given, all specified layers will be MoE layers.
net = SoftMoEVisionTransformer(
moe_layer_index=[0, 2, 4], # Blocks 0, 2 and 4
depth=12,
)
- Note:
moe_layer_index
uses 0-index convention.
The SoftMoELayerWrapper
class can be used to make any network layer, that takes a tensor of shape [batch, length, dim]
, into a Soft Mixture of Experts layer.
import torch
import torch.nn as nn
from soft_moe import SoftMoELayerWrapper
x = torch.rand(1, 16, 128)
layer = SoftMoELayerWrapper(
dim=128,
slots_per_expert=2,
num_experts=32,
layer=nn.Linear,
# nn.Linear arguments
in_features=128,
out_features=32,
)
y = layer(x)
layer = SoftMoELayerWrapper(
dim=128,
slots_per_expert=1,
num_experts=16,
layer=nn.TransformerEncoderLayer,
# nn.TransformerEncoderLayer arguments
d_model=128,
nhead=8,
)
y = layer(x)
- Note: If the name of a layer argument overlaps with one of other arguments (e.g.
dim
) you can pass a partial function tolayer
.- e.g.
layer=partial(MyCustomLayer, dim=128)
- e.g.
@article{puigcerver2023sparse,
title={From Sparse to Soft Mixtures of Experts},
author={Puigcerver, Joan and Riquelme, Carlos and Mustafa, Basil and Houlsby, Neil},
journal={arXiv preprint arXiv:2308.00951},
year={2023}
}