[Kernels] Add fp8 support to reshape_and_cache_flash

[Yard1]

Preparation for fp8 support in flash attn-based backends.

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[robertgshaw2-neuralmagic]

cc @mgoin @tlrmchlsmth

[robertgshaw2-neuralmagic]

👀 @Yard1 can you coordinate with @mgoin here? He has been working on separating k_scale and v_scale in the ckpts

[tlrmchlsmth]

lgtm -- looks straightforward, following along with reshape_and_cache_kernel is doing.

[mgoin]

Hey @Yard1 thanks for sharing - I was working on enabling fp8 for flashinfer in a similar fashion. However I put a pause on the integration to better understand why the performance didn't match expectations. Please check out this sheet of benchmarks. I took the benchmarking configuration directly from the FP8 section of their launch blog. Do you have an idea of why the A100 performance seems so bad in my benchmark?

[Yard1]

@mgoin I hadn't looked into that myself yet. Have you tried asking the flashinfer author in flashinfer repo?

[rkooo567]

is there no case "auto" becomes fp8?

[rkooo567]

besides, handling auto here is a little ugly.. (feel like the caller should convert it alrady) but don't need to handle it here

[Yard1]

I am just following the pattern laid out in the non-flash function

[comaniac]

"auto" now means following the model weights/checkpoints. We should definitely improve the logic in the future.