[KV Offload] Gate packed HMA KV cache on cross-layer config

[LucasWilkinson]

Summary

• Use kv_connector_extra_config["enable_cross_layers_blocks"] to opt multi-group HMA layouts into packed KV allocation as laid out in https://docs.vllm.ai/en/stable/features/nixl_connector_usage/#cross-layers-blocks
• Keep DeepSeek V4-style UniformTypeKVCacheSpecs layouts on the packed path by default.
• Remove the extra VLLM_USE_PACKED_HMA_KV_CACHE environment flag.
• Canonicalize packed KV caches in the offloading worker as one full-row tensor/ref per KV group.

Tests

• UCX_TLS=cuda_ipc,cuda_copy,sm,tcp,self UCX_NET_DEVICES=all .venv/bin/python -m pytest tests/v1/core/test_contiguous_kv_packing.py tests/v1/kv_connector/unit/offloading_connector/test_worker.py tests/v1/kv_connector/unit/test_nixl_simple_cpu_offload.py -q
  • 20 passed, 21 warnings
• .venv/bin/pre-commit run ruff-check --files vllm/v1/core/kv_cache_utils.py vllm/envs.py tests/v1/core/test_contiguous_kv_packing.py vllm/distributed/kv_transfer/kv_connector/v1/offloading/worker.py
  • passed

Benchmarks / runtime validation

DeepSeek V4 P/D (nvidia/DeepSeek-V4-Flash-NVFP4), run:

• Packed KV registered on all prefill/decode ranks with num_regions=1.
• Benchmark: 16/16 successful requests.
• NIXL: 17 transfers, 141 descriptors, 505,284,480 bytes transferred.

GPT-OSS P/D (openai/gpt-oss-20b) with TRITON_ATTN and cross-layer packed config:

• Packed KV registered on prefill/decode with num_regions=1.
• Benchmark: 32/32 successful requests.
• Perf: 3.72 req/s, 59.66 output tok/s, mean TTFT 372.02 ms, mean TPOT 32.81 ms, mean ITL 32.72 ms.
• NIXL: 33 transfers, 279 descriptors, 438,829,056 bytes transferred.

AI assistance

AI assistance was used to prepare this change. This has been reviewed by the submitter.

[orozery]

Can we apply the packed layout for dense models as well?
i.e. remove the len(kv_cache_groups) > 1 check

[LucasWilkinson]

iiuc cross layer kv-cache is already supported for non-hybrid models (please correct me if im wrong); id prefer to keep this PR limited in scope since its really just a temporary solution while we complete #42082

[orozery]

Cross layers de facto no longer works since more and more uniform models (e.g. llama, qwen) use MRV2, which does not have it.

[LucasWilkinson]

Happy to do this as a follow-up but hoping to get this in for 0.24 so I'd rather not increase the scope of this PR (was just intended to be a cleanup fast follow for #46205)