perf: add fused CUDA rotary embedding kernel

- Single-kernel rotary embedding (cos/sin lookup + rotation) replaces PyTorch complex-multiply path (3 kernel launches + f32 upcast per call)
- RotaryEmbedding now stores cos_table/sin_table and returns (cos, sin) f32 tuple instead of a complex tensor
- apply_rotary_emb in rotary_backend.py auto-dispatches: CUDA kernel if available, else torch complex-multiply fallback; backend-agnostic (both attention backends benefit)
- Kernel: 256-thread blocks, grid-stride loop, vectorized __nv_bfloat162 load/store, f32 compute, bf16 out
- Standalone kernel 6-9x faster than torch across decode/prefill shapes, max diff 0 (decode) to 3e-2 (large prefill, bf16)
- Benchmark (L20, bf16, CUDA backend): B=1 9.48->7.25ms (+31%), B=4 10.73->7.67ms (+40%), B=8 10.77->7.81ms (+38%), B=16 10.79->7.83ms (+38%)

astrai/extension/__init__.py

@@ -30,6 +30,7 @@ from astrai.extension.attention_ops import (
     attn_prefill,
 )
 from astrai.extension.loader import KERNEL_NAMES, is_available
+from astrai.extension.rotary_backend import apply_rotary_emb
 
 __all__ = [
     "ATTN_BACKEND",
@@ -44,4 +45,5 @@ __all__ = [
     "attn_prefill",
     "is_available",
     "KERNEL_NAMES",
+    "apply_rotary_emb",
 ]