(WIP) Support targeting the embedding layer for LoRA

[ajtejankar]

What does this PR do?

1. Re-organize the code in BatchLoraWeights.load. This function was a bit hard to understand as there were multiple list comprehensions with almost same looping logic. So, merged all of them into two loops for improved clarity. @tgaddair Can you confirm if this looks good? I can revert back to the original code in case this change can cause problems.
2. (WIP) Support embedding layer as a target module. This is mostly done except multi-gpu inference.

[ajtejankar]

@tgaddair I am pushing a partially done commit that supports embedding layer loras.

• [x] Similar to HF implementation, lora_A is used for embedding lookup while lora_B is multiplied
• [x] Prevents lora_A transpose when in BGMV mode
• [x] Contains two implementations to replace two kernels: SGMV and BGMV
• [ ] Both are implemented with for loops. How can we optimize them?
• [ ] Cannot handle multi-GPU. I will need some help understanding sharding in LoRAX as I found it confusing. :(
• [ ] Tested crudely by comparing with generation from HF, but need to add a proper test case.