[2/N][KV-Cache Layout Refactor] Pack K/V into the content dim across attention backends

[LucasWilkinson]

PR #42374 (first part of RFC #42082) has been split into 4 PRs:

#44454 [1/N][KV-Cache Layout Refactor] Refactor DSV4 KV cache config
-> #44455 [2/N][KV-Cache Layout Refactor] Pack K/V into the content dim across attention backends
#44456 [3/N][KV-Cache Layout Refactor] Standardize Mamba cache; drop get_transfer_cache_regions
#44458 [4/N][KV-Cache Layout Refactor] Standardize KV cache layout

Summary

Packs K and V into the content dimension across attention backends, changing the KV cache shape from (num_blocks, 2, block_size, num_kv_heads, head_size) (separate leading K/V dim) to (num_blocks, num_kv_heads, block_size, 2*head_size). Backends recover K/V via kv_cache.transpose(1, 2).split(head_size, dim=-1). This removes the need for get_kv_cache_stride_order() in most paths and is a major diff reduction toward #42374.

Stacked on top of #44454 (land that first)

Testing

pytest tests/v1/attention/test_attention_backends.py -v
pytest tests/kernels/attention/test_cache.py -v
pytest tests/v1/worker/test_gpu_model_runner.py -v

Most backend paths require specific GPUs (ROCm, nvfp4/flashinfer); the submitter will run the relevant subset on appropriate hardware before merge.

AI assistance

This PR was prepared with AI assistance (Claude).