📝 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>

tests/test_xattn_estimate_alignment.py

@@ -226,6 +226,14 @@ for q_chunk_idx in range(q_chunk_num):
     print(f"  Q chunk {q_chunk_idx}: merged {kv_chunk_num} KV chunks, attn_sum shape={attn_sum_concat.shape}")
 
 mask_kv_chunking = torch.cat(simple_mask_list, dim=2)
+
+# 应用与 xattn_estimate 相同的 causal mask 后处理 (xattn.py 第 1300-1306 行)
+mask_kv_chunking[:, :, -q_block_num:, -q_block_num:] = torch.where(
+    torch.tril(torch.ones(q_block_num, q_block_num, dtype=bool, device=device), diagonal=0),
+    mask_kv_chunking[:, :, -q_block_num:, -q_block_num:],
+    False,
+)
+
 mask_kv_chunking_valid = mask_kv_chunking[:, :, :q_blocks, :k_blocks]
 selected_kv = (mask_kv_chunking_valid & causal_mask.unsqueeze(0).unsqueeze(0)).sum().item()
 density_kv = selected_kv / total_api