speculative: copy_kv_position primitive for tree drafting KV remap

SGLang-style "write-all, copy-move on acceptance" approach: after tree
verification, physically copy an accepted sibling's K/V from its
physical cache slot to the canonical sequential position.

New CUDA kernel: copy_kv_position_kernel in reshape_and_cache.cu.
For one token (src_pos → dst_pos), copies head_dim × num_kv_heads BF16
elements in both K and V pools. Grid = num_kv_heads, block = head_dim.
Cost for one token across 36 layers: ~5.3 MB D2D copy @ 900 GB/s = <6μs.

Rust FFI: copy_kv_position(k_pool, v_pool, block_ids, src_pos, dst_pos,
num_kv_heads, head_dim, block_size, stream).

PagedKVCache method: copy_kv_position(slot, src_pos, dst_pos) — uploads
block_ids for the sequence, calls the kernel per layer. This is the
primitive needed by tree drafting: when a non-primary sibling at cache
position P+2 is accepted as the "true" token for target position P+1,
call copy_kv_position(slot, P+2, P+1) then truncate to P+2.

Next: wire into bench-eagle3 tree drafting loop with top-2 siblings.

crates/xserv-kernels/src/lib.rs

@@ -15,9 +15,9 @@ pub mod transpose;
 pub use activation::{add, bias_add_2d, gelu, gpt_oss_glu, mul, scale, silu, silu_mul};
 pub use argmax::{argmax_bf16_single, argmax_bf16_to_host};
 pub use attention::{
-    attention, decode_attention, flash_attention, flash_attention_sinks, paged_decode_attention,
-    paged_decode_attention_sinks, paged_decode_attention_tree, reshape_and_cache_batched_bf16,
-    reshape_and_cache_bf16,
+    attention, copy_kv_position, decode_attention, flash_attention, flash_attention_sinks,
+    paged_decode_attention, paged_decode_attention_sinks, paged_decode_attention_tree,
+    reshape_and_cache_batched_bf16, reshape_and_cache_bf16,
 };
 pub use embedding::{embedding, embedding_device_ids};
 pub use gemm::{GemmBackend, batched_matmul, matmul, matmul_batched_gemv};