refactor: remove bf16 gemm and swiglu kernels and rebuild csrc benchmarks

- delete csrc/kernels/gemm.cu and swiglu.cu and drop their CMake and setup.py registration
- remove the ops wrappers plus backend/linear.py and backend/swiglu.py so Linear and MLP call F.linear directly
- drop the four gemm and swiglu kernel test files and prune the stale cuda_kernels.md sections
- add csrc/bench benchmarks for the remaining kernels: attention decode prefill paged decode paged prefill versus single-launch SDPA references, rotary versus the torch fallback, fp8 quantize and mm_fp8 versus torch baselines
- attention, rotary_emb, and fp8_ops kernels are unchanged

astrai/extension/__init__.py

@@ -1,11 +1,10 @@
 """CUDA kernel wrappers, operator dispatch, and backend selection.
 
 Public API:
-    - ``attention``, ``linear``, ``swiglu``, ``apply_rotary_emb`` — op
-      families with safe torch fallbacks (see ``astrai.extension.backend``)
+    - ``attention``, ``apply_rotary_emb`` — op families with safe torch
+      fallbacks (see ``astrai.extension.backend``)
     - ``attn_decode`` / ``attn_prefill`` / ``attn_paged_decode`` /
       ``attn_paged_prefill`` — direct attention kernel wrappers
-    - ``bf16_gemm`` / ``bf16_swiglu`` — directly callable linear/MLP kernels
     - ``AttentionBackend`` / ``TorchNativeBackend`` / ``CudaBackend`` /
       ``FlashAttnBackend`` — attention backend strategies
     - ``resolve`` / ``explain`` / ``op_backend`` / ``env_mode`` — the shared
@@ -14,6 +13,9 @@ Public API:
 Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
 (blhd). Scale is always ``1/sqrt(head_dim)``. Wrapper functions call their
 compiled CUDA kernels directly; fallback is the backend's responsibility.
+Linear projections and dense-MLP SwiGLU run plain torch (``F.linear`` /
+``Linear`` / ``MLP``); the former bf16_gemm / bf16_swiglu kernels and their
+backends were removed.
 """
 
 from astrai.extension.backend import (
@@ -27,8 +29,6 @@ from astrai.extension.backend import (
     attention,
     attn_backend,
     get_backend,
-    linear,
-    swiglu,
 )
 from astrai.extension.dispatch import (
     Axes,
@@ -52,9 +52,8 @@ from astrai.extension.ops import (
     TensorLayout,
     attn_decode,
     attn_paged_decode,
+    attn_paged_prefill,
     attn_prefill,
-    bf16_gemm,
-    bf16_swiglu,
 )
 
 __all__ = [
@@ -68,13 +67,10 @@ __all__ = [
     "attention",
     "attn_backend",
     "get_backend",
-    "linear",
-    "swiglu",
     "attn_decode",
     "attn_paged_decode",
     "attn_prefill",
-    "bf16_gemm",
-    "bf16_swiglu",
+    "attn_paged_prefill",
     "is_available",
     "KERNEL_NAMES",
     "apply_rotary_emb",