pytorch-mixtures 0.1.3

The one-stop solution to easily integrate MoE & MoD layers into custom PyTorch code.

PyTorch Mixtures [PyPi]

A plug-and-play module for Mixture-of-Experts and Mixture-of-Depths in PyTorch. Your one-stop solution for inserting MoE/MoD layers into custom neural networks effortlessly!

--

Sources:

1. Sparse Mixture of Experts, 2017
2. Mixture of Depths, 2024

Features/Todo

• Mixture of Experts
  • Top-k Routing
  • Expert Choice Routing
  • router-z loss
  • load-balancing loss
  • Testing of all MoE protocols - finished
• Mixture of Depths
  • capacity-based routing around attention layer
  • Testing of MoD protocol - finished

Installation

Simply using pip3 install pytorch-mixtures will install this package. Note that this requires torch and einops to be pre-installed as dependencies. If you would like to build this package from source, run the following command:

git clone https://github.com/jaisidhsingh/pytorch-mixtures.git
cd pytorch-mixtures
pip3 install .

Usage

pytorch-mixtures is designed to effortlessly integrate into your existing code for any neural network of your choice, for example

from pytorch_mixtures.routing import ExpertChoiceRouter
from pytorch_mixtures.moe_layer import MoELayer

import torch
import torch.nn as nn


# define some config
BATCH_SIZE = 16
SEQ_LEN = 128
DIM = 768
NUM_EXPERTS = 8
CAPACITY_FACTOR = 1.25

# first initialize the router
router = ExpertChoiceRouter(dim=DIM, num_experts=NUM_EXPERTS)

# choose the experts you want: pytorch-mixtures just needs a list of `nn.Module` experts
# for e.g. our experts are just linear layers
experts=[nn.Linear(DIM, DIM) for _ in range(NUM_EXPERTS)]

# supply the router and experts to the MoELayer for modularity
moe = MoELayer(
    num_experts=NUM_EXPERTS, 
    router=router, 
    experts=experts, 
    capacity_factor=CAPACITY_FACTOR
)

# initialize some test input
x = torch.randn(BATCH_SIZE, SEQ_LEN, DIM)

# pass through moe
moe_output, aux_loss, router_z_loss = moe(x) # shape: [BATCH_SIZE, SEQ_LEN, DIM]

You can also use this easily within your own nn.Module classes

from pytorch_mixtures.routing import ExpertChoiceRouter
from pytorch_mixtures.moe import MoELayer
from pytorch_mixtures.utils import MHSA # multi-head self-attention layer provided for ease
import torch
import torch.nn as nn


class CustomMoEAttentionBlock(nn.Module):
    def __init__(self, dim, num_heads, num_experts, capacity_factor, experts):
        super().__init__()
        self.attn = MHSA(dim, num_heads)
        router = ExpertChoiceRouter(dim, num_experts)
        self.moe = MoELayer(dim, router, experts, capacity_factor)
        
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
    
    def forward(self, x):
        x = self.norm1(self.attn(x) + x)
        moe_output, aux_loss, router_z_loss = self.moe(x)
        x = self.norm2(moe_output + x)
        return x, aux_loss, router_z_loss


experts = [nn.Linear(768, 768) for _ in range(8)]
my_block = CustomMoEAttentionBlock(
    dim=768,
    num_heads=8,
    num_experts=8,
    capacity_factor=1.25,
    experts=experts
)

# some test input
x = torch.randn(16, 128, 768)
output, aux_loss, router_z_loss = my_block(x) # output shape: [16, 128, 768]

Testing

This package provides the user to run a simple yet reliable absl test for the MoE code. If all experts are initialized as the same module, the output of the MoELayer should be equal to the input tensor passed through any expert. Both ExpertChoiceRouter and TopkRouter are tested thusly, and succeed in the tests. The users can run these tests for themselves by running the following:

from pytorch_mixtures import run_tests

run_tests()

Note: All tests pass correctly. If a test fails, it is likely due to an edge case in the random initializations. Try again, and it will pass.

Citation

If you found this package useful, please cite it in your work:

@misc{JaisidhSingh2024,
  author = {Singh, Jaisidh},
  title = {pytorch-mixtures},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/jaisidhsingh/pytorch-mixtures}},
}

References

This package was built with the help of open-source code mentioned below:

1. Google Flaxformer
2. ST-MoE by Lucidrains