Fix fp16 underflow in MoE load balancing loss by enforcing fp32 softmax
Author: ITcarrot
Pull request: yes

# What does this PR do?

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This PR updates the load_balancing_loss_func across various MoE models to explicitly perform the softmax operation in float32.

When running models in fp16, aux_loss underflow to 0 when using large batchsize. The underflow comes from doing the flowing operation in fp16:
```python
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
        router_per_expert_attention_mask, dim=0
)
```

Casting fp32 in softmax can ensure a precise and robust load balancing loss calculation.


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@Cyrilvallez @IlyasMoutawwakil

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Comments:
**[For maintainers]** Suggested jobs to run (before merge)

run-slow: dbrx, ernie4_5_moe, ernie4_5_vl_moe, flex_olmo, glm4v_moe, gpt_oss, granitemoe, granitemoehybrid, granitemoeshared, jamba, jetmoe, minimax, minimax_m2, mixtral, olmoe, phimoe