mixture-of-experts

Sparsely Gated Mixture of Experts - Pytorch

The Sparsely Gated Mixture of Experts is a Pytorch implementation that significantly enhances the capacity of language models—with an increased parameter count—while maintaining constant computational requirements. This implementation largely mirrors the Tensorflow version with some improvements and is accessible for those familiar with Python.

Installation

To get started with the Mixture of Experts, users can easily install it via pip:

$ pip install mixture_of_experts

How It Works

This concept builds on the idea of using multiple expert models to solve a specific task more efficiently. In typical neural networks, computation often scales linearly with the number of model parameters. However, by using sparsely gated experts, only a subset of these experts are activated during prediction, keeping computation cost low yet performance high.

Usage Overview

With the Pytorch package, users can construct and employ a Mixture of Experts model as follows:

import torch
from torch import nn
from mixture_of_experts import MoE

moe = MoE(
    dim=512,
    num_experts=16,                   # Number of experts, improves model capacity
    hidden_dim=512 * 4,               # Hidden layer size in each expert
    activation=nn.LeakyReLU,          # Option for different activation functions; defaults to GELU
    second_policy_train='random',     # Policy for second-place expert usage in training
    second_policy_eval='random',      # Policy for evaluation
    second_threshold_train=0.2,
    second_threshold_eval=0.2,
    capacity_factor_train=1.25,       # Buffer capacity to handle imbalanced gates
    capacity_factor_eval=2.0,         # Capacity setting during evaluation
    loss_coef=1e-2                    # Auxiliary loss for balancing experts
)

inputs = torch.randn(4, 1024, 512)
out, aux_loss = moe(inputs)  # Outputs include the processed data and auxiliary loss 

Advanced Hierarchical Structure

For those requiring a more complex setup, a hierarchical mixture of experts can be used. This involves multiple layers of experts to mimic strategies like those employed in the GShard initiative:

import torch
from mixture_of_experts import HeirarchicalMoE

moe = HeirarchicalMoE(
    dim=512,
    num_experts=(4, 4)  # First layer with 4 gates, each leading to 4 experts, totaling 16 experts
)

inputs = torch.randn(4, 1024, 512)
out, aux_loss = moe(inputs)

Handling Billion-Parameter Models

With efficient implementation, users can experiment with massive models, such as a billion-parameter neural network, using hierarchical configurations:

moe = HeirarchicalMoE(
    dim=512,
    num_experts=(22, 22)
).cuda()  # Ensures operations are performed on GPU for large-scale models

inputs = torch.randn(1, 1024, 512).cuda()
out, aux_loss = moe(inputs)

total_params = sum(p.numel() for p in moe.parameters())
print(f'number of parameters - {total_params}')

Custom Expert Networks

For more customization, one can design sophisticated network structures for the experts and integrate them:

class Experts(nn.Module):
    def __init__(self, dim, num_experts=16):
        super().__init__()
        self.w1 = nn.Parameter(torch.randn(num_experts, dim, dim * 4))
        self.w2 = nn.Parameter(torch.randn(num_experts, dim * 4, dim * 4))
        self.w3 = nn.Parameter(torch.randn(num_experts, dim * 4, dim))
        self.act = nn.LeakyReLU(inplace=True)

    def forward(self, x):
        hidden1 = self.act(torch.einsum('end,edh->enh', x, self.w1))
        hidden2 = self.act(torch.einsum('end,edh->enh', hidden1, self.w2))
        return torch.einsum('end,edh->enh', hidden2, self.w3)

experts = Experts(512, num_experts=16)
moe = MoE(dim=512, num_experts=16, experts=experts)

inputs = torch.randn(4, 1024, 512)
out, aux_loss = moe(inputs)

Conclusion

The Sparsely Gated Mixture of Experts in Pytorch provides a scalable and powerful method for enhancing neural network capabilities while controlling computational costs. It offers both standard and customized implementations, making it versatile for researchers and developers aiming for high-performance models in natural language processing or other computational fields.