📝 docs: add storage overhead analysis and batch tests for KV chunking

- Update xattn_kv_chunking_kernels.md with:
  - Detailed storage overhead analysis (O(S) vs O(S²))
  - Peak memory optimization (8x reduction)
  - Support for independent Q/KV chunk sizes
  - Batch verification results (3K-64K seqlen)
  - ASCII pipeline diagram

- Add test_xattn_kv_chunking_batch.py for batch validation
- Fix causal mask post-processing in alignment test
- Update CLAUDE.md documentation index

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Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

CLAUDE.md

@@ -16,7 +16,7 @@ Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline L
 | [`docs/sparse_attention_guide.md`](docs/sparse_attention_guide.md) | Block sparse attention methods (XAttention, FlexPrefill, MInference, AvgPool, Quest), computation flow, algorithms |
 | [`docs/xattention_algorithm_guide.md`](docs/xattention_algorithm_guide.md) | XAttention 算法详解: stride reshape、Triton kernels、BSA 依赖、块选择算法 |
 | [`docs/xattn_kernels_guide.md`](docs/xattn_kernels_guide.md) | XAttention Triton kernels: flat_group_gemm (反对角线求和)、softmax_fuse_block_sum (block 聚合) |
-| [`docs/xattn_kv_chunking_kernels.md`](docs/xattn_kv_chunking_kernels.md) | XAttention KV Chunking: 三阶段 softmax (partial stats + merge + normalize)，支持 KV 维度分块 |
+| [`docs/xattn_kv_chunking_kernels.md`](docs/xattn_kv_chunking_kernels.md) | XAttention KV Chunking: 三阶段 softmax、存储开销分析 (O(S) vs O(S²))、峰值显存优化 (8x)、Q/KV 独立分块 |
 | [`docs/xattn_chunked_prefill.md`](docs/xattn_chunked_prefill.md) | XAttention chunked prefill: API、使用方式、一致性要求 |
 | [`docs/xattn_bsa_policy_design.md`](docs/xattn_bsa_policy_design.md) | XAttention BSA Policy: 算法设计、性能基准(128K)、内存管理、density 统计 |
 | [`docs/xattn_density_benchmark.md`](docs/xattn_density_benchmark.md) | 📊 XAttention Density Benchmark: 4K-32K context、stride 参数、per-layer density 分析 |