Fused ROPE and reshape cache kernel

[maleksan85]

Added support to ROPE kernel to store values right into cache.

LLama3.1 8B fp16 test without using fused ROPE:

root@banff-cyxtera-s82-5:~/workspace/vllm# HIP_VISIBLE_DEVICES=5 VLLM_FUSED_ROPE_W_KV_CACHE=0 python benchmarks/benchmark_latency.py --model /data/models/Meta-Llama-3.1-8B --input_len 1024 --output_len 1024 --batch-size=16

Avg latency: 9.835484731818239 seconds

with fused kernel:

root@banff-cyxtera-s82-5:~/workspace/vllm# HIP_VISIBLE_DEVICES=5 VLLM_FUSED_ROPE_W_KV_CACHE=1 python benchmarks/benchmark_latency.py --model /data/models/Meta-Llama-3.1-8B --input_len 1024 --output_len 1024 --batch-size=16

Avg latency: 9.86210185677434 seconds

Also perf in test (although it is not very fair as kernels were run only once). Taking some test in the end of run of test suite:

tests/kernels/test_fused_rope_and_reshape_cache.py::test_fused_rotary_embedding_with_reshape_cache[5-0-False-dtype1-None-128-8-32-16-8-auto-1024-16] Non fused call 0.22136466577649117 ms
Fused run 0.0467388890683651 ms

Shapes are pretty small, so kernel start overhead might be sufficient.