[vllm] Add support for FP8 in Triton FA kernel

[ilia-cher]

Adding support for FP8 (E4M3) in Triton FA kernel, including per-tensor scaling factors.

Test:

1. Patched rocm_flash_attn.py to call FA kernel with scaling factors
   (https://gist.github.com/ilia-cher/216762889331cefeb158634a651b2fac)

2. Ran the benchmark:
   python3 benchmark_latency.py --model \
      /data/models/Llama-3.1-8B-Instruct-FP8-KV \
      --input-len 8192 \
      --output-len 1 \
      --batch-size 32 \
      --enforce-eager \
      --num-iters 10 \
      --num-iters-warmup 2 \
      --enable-chunked-prefill False \
      --dtype float16
Before:
Avg latency: 6.418297152221203 seconds
10% percentile latency: 6.380122036673129 seconds
25% percentile latency: 6.390297698322684 seconds
50% percentile latency: 6.404989298898727 seconds
75% percentile latency: 6.421127524343319 seconds
90% percentile latency: 6.4394324975088235 seconds
99% percentile latency: 6.562963163470849 seconds

After:
Avg latency: 5.162057781498879 seconds
10% percentile latency: 5.1219399653142315 seconds
25% percentile latency: 5.135780334530864 seconds
50% percentile latency: 5.151887209853157 seconds
75% percentile latency: 5.158517300733365 seconds
90% percentile latency: 5.184290232090279 seconds
99% percentile latency: 5.314461483638734 seconds

3. (Sanity) check using
   https://gist.github.com/ilia-cher/951a3d011a8bafa7c5180fbc3a151a57

4. (follow up in scaling factors loading PR) P3L perplexity check

[shajrawi]

Brilliant work Ilia, Kudos!