Add Balance Loss to MoE Example for Enhanced Expert Load Distribution (Issue #1300)

[Mukku27]

Add Balance Loss to MoE Example for Enhanced Expert Load Distribution (Issue #1300)

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What changes were proposed in this pull request?

This pull request proposes the integration of a balance loss mechanism into the Mixture-of-Experts (MoE) example in the atorch codebase. Specifically, an auxiliary loss has been added to the TopNRouter class to facilitate balanced load distribution across experts, improving model performance and efficiency. Key modifications include:

1. Router Updates:

   • Updated the TopNRouter class to compute and return an auxiliary loss based on router probabilities, helping distribute tokens more evenly.
   • Added _compute_auxiliary_loss() method to calculate this auxiliary loss, which is currently set up to use the mean of router probabilities as a placeholder. This can be customized based on specific balancing requirements.

2. MoE Layer Enhancements:

   • Modified the _SparseMLP class to incorporate auxiliary loss from the router and propagate it back, enhancing the MoE layer’s load-balancing capabilities.

3. Training Loop Modifications:

   • Integrated the auxiliary loss into the training loop by including it in the total loss computation. This ensures that the auxiliary loss is factored into backpropagation, aiding in balanced expert utilization.

4. Auxiliary Loss Weight Configurability:

   • Added a command-line argument, --aux_loss_weight, to allow users to adjust the weight of the auxiliary loss as needed. This flexibility enables fine-tuning of the loss function based on model requirements.

Why are the changes needed?

These changes address issue #1300 by introducing an auxiliary balance loss mechanism to the MoE example, which aims to improve the distribution of workload across experts. In multi-expert architectures, load imbalances can lead to inefficiencies and underutilized resources, ultimately impacting model performance. The proposed auxiliary loss provides a straightforward way to mitigate these imbalances, enhancing both training efficiency and overall model effectiveness.

Does this PR introduce any user-facing change?

Yes, this PR introduces a new command-line argument, --aux_loss_weight, which allows users to adjust the weight of the auxiliary loss as needed. By default, it is set to 0.01 but can be configured according to specific model or training needs.

How was this patch tested?

The patch was tested through the following steps:

• Functional Testing: Verified that the auxiliary loss is calculated and returned as expected in the TopNRouter class and is correctly incorporated into the MoE layer.
• Integration Testing: Confirmed that the training loop correctly computes total loss with the auxiliary loss and backpropagates without errors.
• Command-line Argument Testing: Ensured that the --aux_loss_weight argument correctly adjusts the auxiliary loss weight in different runs.

These changes are expected to contribute to improved expert load balancing, benefiting users who require scalable and efficient MoE models.