[Bugfix] Restore unloaded FP8 scale params during layerwise reload

[aoshen02]

Summary

• Fix NaN inference output when external weight sync provides FP8 weights without scale_inv tensors during layerwise reload
• After replaying buffered weights, restore unloaded scale parameters from previously saved kernel tensors

Root cause

When RL training frameworks sync FP8 MoE weights to vLLM via start_weight_update/update_weights, they may send only the weight data without scale_inv tensors. During layerwise reload:

1. materialize_layer allocates scale params as torch.empty (contains NaN)
2. Weight replay only loads the params that were actually sent — scale params stay NaN
3. process_weights_after_loading reads NaN scales and passes them to the MoE kernel
4. All subsequent inference produces NaN

The fix checks for unloaded parameters after replay. If they contain NaN and previously saved kernel tensors exist (from before reload), it restores the valid scale values.

Not duplicating existing PRs

• Layerwise reload crashes with CUDA illegal memory access on compressed-tensors channel-wise FP8 MoE #41670 reports the same bug family (layerwise + FP8 MoE) but no fix PR exists
• [QeRL] Compose online quantization with quantized reloading #38032 / Revert "[QeRL] Compose online quantization with quantized reloading" (#38032) #38446 (QeRL) addressed a different aspect (online quantization composition) and was reverted

Test plan

• Reproduce: load FP8 MoE model, call start_weight_update + update_weights with weights only (no scale_inv), verify NaN before fix
• After fix: same flow produces valid inference output
• Normal checkpoint reload (safetensors with scale_inv) still works unchanged

AI assistance disclosure

This fix was developed with AI assistance (Claude). The human submitter has reviewed all changed lines and understands the fix end-to-end.

Related: #41670