Implement the load-balancing aux loss and a training step demonstrating it equalizes exper

Hard

Mixture-of-Experts3 / 4

Chapter 083 / 4

Mixture-of-Experts

Implement the load-balancing aux loss and a training step demonstrating it equalizes exper

Hard

Implement the load-balancing aux loss and a training step demonstrating it equalizes expert usage on synthetic data.

Target interface

load_balance_loss(logits, k, n_experts)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

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

def load_balance_loss(logits, k, n_experts):
    raise NotImplementedError

def demo():
    raise NotImplementedError

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Chapter 083 / 4

Mixture-of-Experts

Implement the load-balancing aux loss and a training step demonstrating it equalizes exper

Hard

Implement the load-balancing aux loss and a training step demonstrating it equalizes expert usage on synthetic data.

Target interface

load_balance_loss(logits, k, n_experts)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.