moe-training

MoE Training: Mixture of Experts

When to Use This Skill

Use MoE Training when you need to:

• Train larger models with limited compute (5× cost reduction vs dense models)
• Scale model capacity without proportional compute increase
• Achieve better performance per compute budget than dense models
• Specialize experts for different domains/tasks/languages
• Reduce inference latency with sparse activation (only 13B/47B params active in Mixtral)
• Implement SOTA models like Mixtral 8x7B, DeepSeek-V3, Switch Transformers

Notable MoE Models: Mixtral 8x7B (Mistral AI), DeepSeek-V3, Switch Transformers (Google), GLaM (Google), NLLB-MoE (Meta)

Installation

# DeepSpeed with MoE support
pip install deepspeed>=0.6.0

# Megatron-DeepSpeed for large-scale training
git clone https://github.com/microsoft/Megatron-DeepSpeed
cd Megatron-DeepSpeed
pip install -r requirements.txt

# Alternative: HuggingFace Transformers
pip install transformers accelerate

Quick Start

Basic MoE Architecture

import torch
import torch.nn as nn

class MoELayer(nn.Module):
    """Sparse Mixture of Experts layer."""

    def __init__(self, hidden_size, num_experts=8, top_k=2):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k

        # Expert networks (FFN)
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_size, 4 * hidden_size),
                nn.GELU(),
                nn.Linear(4 * hidden_size, hidden_size)
            )
            for _ in range(num_experts)
        ])

        # Gating network (router)
        self.gate = nn.Linear(hidden_size, num_experts)

    def forward(self, x):
        # x shape: (batch_size, seq_len, hidden_size)
        batch_size, seq_len, hidden_size = x.shape

        # Flatten for routing
        x_flat = x.view(-1, hidden_size)  # (batch_size * seq_len, hidden_size)

        # Compute gate scores
        gate_logits = self.gate(x_flat)  # (batch_size * seq_len, num_experts)

        # Top-k routing
        gate_scores = torch.softmax(gate_logits, dim=-1)
        topk_scores, topk_indices = torch.topk(gate_scores, self.top_k, dim=-1)

        # Normalize top-k scores
        topk_scores = topk_scores / topk_scores.sum(dim=-1, keepdim=True)

        # Dispatch and combine expert outputs
        output = torch.zeros_like(x_flat)

        for i in range(self.top_k):
            expert_idx = topk_indices[:, i]
            expert_scores = topk_scores[:, i].unsqueeze(-1)

            # Route tokens to experts
            for expert_id in range(self.num_experts):
                mask = (expert_idx == expert_id)
                if mask.any():
                    expert_input = x_flat[mask]
                    expert_output = self.experts[expert_id](expert_input)
                    output[mask] += expert_scores[mask] * expert_output

        # Reshape back
        return output.view(batch_size, seq_len, hidden_size)