[Kernel] Flash Attention 3 Support

[LucasWilkinson]

Most of the changes for FA3 are in vllm-flash-attn, but there was some changes required on vLLM side. Namely FA3 doesn't support cu_seqlens_k when using a paged kv cache, instead it uses seqused_k which is the kv seqlens. FA2 also supports seqused_k so we switch to using this for both FA3 and FA2 when dealing with a paged kv-cache in-order to maintain a common interface.

This currently mainly improves V1 performance, some throughput numbers:

env VLLM_FLASH_ATTN_VERSION=2 VLLM_USE_V1=1 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 
Throughput: 12.81 requests/s, 27872.21 total tokens/s, 1639.54 output tokens/s

env VLLM_FLASH_ATTN_VERSION=3 VLLM_USE_V1=1 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 
Throughput: 14.24 requests/s, 30986.15 total tokens/s, 1822.71 output tokens/s

env VLLM_FLASH_ATTN_VERSION=2 VLLM_USE_V1=0 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 
Throughput: 12.40 requests/s, 26991.51 total tokens/s, 1587.74 output tokens/s

env VLLM_FLASH_ATTN_VERSION=3 VLLM_USE_V1=0 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 
Throughput: 12.74 requests/s, 27714.72 total tokens/s, 1630.28 output tokens/s

env VLLM_FLASH_ATTN_VERSION=2 VLLM_USE_V1=0 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 --num-scheduler-steps 4
Throughput: 13.20 requests/s, 28726.44 total tokens/s, 1689.79 output tokens/s

env VLLM_FLASH_ATTN_VERSION=3 VLLM_USE_V1=0 python benchmarks/benchmark_throughput.py --model meta-llama/Llama-3.1-8B-Instruct --input-len 2048 --output-len 128 --max-num-batched-tokens 16384 --num-scheduler-steps 4
Throughput: 13.34 requests/s, 29029.89 total tokens/s, 1707.64 output tokens/s

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🚀

[WoosukKwon]

LGTM. Amazing work! This resolves the performance issue in V1! 🚀
Really appreciate it!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @LucasWilkinson.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork