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/rotary_ops.py

@@ -0,0 +1,46 @@
+"""Rotary embedding CUDA kernel wrapper.
+
+Calls the compiled CUDA kernel directly. If the kernel is not available,
+raises ``RuntimeError``. Fallback to torch complex multiply is the
+responsibility of ``astrai.model.components.rope.apply_rotary_emb``.
+
+Layout convention: x is ``[batch, seq_len, n_heads, head_dim]`` (blhd, bf16).
+cos/sin are ``[batch, seq_len, head_dim/2]`` (f32).
+"""
+
+import torch
+
+from astrai.extension.loader import _available, _modules
+
+
+def _check_available():
+    if not _available.get("rotary_emb"):
+        raise RuntimeError(
+            "CUDA kernel 'rotary_emb' is not available. "
+            "Build with CSRC_KERNELS=true or use the torch fallback."
+        )
+
+
+def rotary_emb(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> torch.Tensor:
+    """Fused rotary embedding kernel.
+
+    Applies rotation: for each pair (x_even, x_odd):
+        out_even = x_even * cos - x_odd * sin
+        out_odd  = x_even * sin + x_odd * cos
+
+    Args:
+        x: [batch, seq_len, n_heads, head_dim] (bf16, contiguous)
+        cos: [batch, seq_len, head_dim/2] (f32)
+        sin: [batch, seq_len, head_dim/2] (f32)
+
+    Returns:
+        [batch, seq_len, n_heads, head_dim] (bf16)
+    """
+    _check_available()
+    if not x.is_contiguous():
+        x = x.contiguous()
+    return _modules["rotary_emb"].rotary_emb(x, cos, sin)