Implement a full sparse MoE FFN with capacity, token dropping, and gate-weighted combinati

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Mixture-of-Experts Models2 / 4

Chapter 082 / 4

Mixture-of-Experts Models

Implement a full sparse MoE FFN with capacity, token dropping, and gate-weighted combinati

Medium

Implement a full sparse MoE FFN with capacity, token dropping, and gate-weighted combination.

Target interface

class Expert

Complete the starter code in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

class Expert(nn.Module):

    def __init__(self, d_model, d_ff):
        raise NotImplementedError

    def forward(self, x):
        raise NotImplementedError

class SparseMoE(nn.Module):

    def __init__(self, d_model, d_ff, n_experts, k, capacity_factor=1.25):
        raise NotImplementedError

    def forward(self, x):
        raise NotImplementedError

Local draft

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Chapter 082 / 4

Mixture-of-Experts Models

Implement a full sparse MoE FFN with capacity, token dropping, and gate-weighted combinati

Medium

Implement a full sparse MoE FFN with capacity, token dropping, and gate-weighted combination.

Target interface

class Expert

Complete the starter code in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.