Add moe loss normalization for RL SFT

[pthombre]

What does this PR do ?

This change adds a separate loss scaling function specifically for MoE (Mixture of Experts) auxiliary losses during RL SFT training.

Problem: Previously, MoE auxiliary losses used the same grad_scale_func as the main model loss. In RL SFT scenarios, you may need to scale the MoE auxiliary loss differently from the primary loss (e.g., the main loss uses a dynamic scaler tied to RL
rewards, but MoE load-balancing losses need their own scaling).

What changed:

• A new optional config field moe_grad_scale_func is added to ModelParallelConfig
• In the forward step, MoE loss scaling now checks for moe_grad_scale_func first, falls back to grad_scale_func if not set, and defaults to 1 otherwise
• Note that moe_grad_scale_func takes no arguments (returns the scale directly), unlike grad_scale_func which takes a tensor input — this allows a simpler interface where the caller pre-computes the scale

Impact: Existing code is unaffected since moe_grad_scale_func defaults to None, preserving the current fallback behavior.

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