Fix illegal memory access caused by missing _kernel_block_sizes attribute in some vLLM versions

[lianghao208]

In vLLM 0.16, gpu_model_runner does not have the self._kernel_block_sizes attribute. As a result, kernel_block_sizes = getattr(self, "_kernel_block_sizes", None) returns None, which causes kvcached to incorrectly assume ratio = 1. This leads to an excessively large stride value being computed, ultimately resulting in an illegal memory access error.

For example:

Variable	Value
Model	Qwen3.5-9B hybrid architecture
kv_cache_group[0,1,2]	MambaSpec
kv_cache_group[3]	FullAttentionSpec, layers=8, block_size=528
num_kv_heads	4
head_size	256
page_size_bytes	2,162,688 > 2MB
virtual block size	528
FA3 kernel_block_size	16 (determined by get_supported_kernel_block_sizes())

Variable	Incorrect Value	Correct Value
kernel_block_size	528	16
ratio	1	33 (= 528 ÷ 16)
kernel_kvcache_shape	[2, 2699, 528, 4, 256]	[2, 89067, 16, 4, 256]
kvcached's perspective	One FA3 kernel block holds 528 tokens, up to 2,699 blocks	One FA3 kernel block holds 16 tokens, up to 89,067 blocks
vllm's perspective	One FA3 kernel block holds 16 tokens, up to 89,067 blocks	One FA3 kernel block holds 16 tokens, up to 89,067 blocks
block dimension stride	1,081,344 illegal memory access!!! (=528*4*256*2)	32,768 (=2*16*4*256)

This PR adds proper handling for the case where _kernel_block_sizes is not defined, ensuring the stride is computed correctly across different vLLM versions.