perf: dispatch linear gemv by decode batch size and unify extension style

- replace the per-shape auto tables in the linear backend with an M-banded rule (M in [2,4] on compute capability 8.0+) that measured at the HBM bandwidth floor across every family, and fold the capability check into the capable guard
- drop the unreachable swiglu auto shape-table machinery so both backends share one env-mode ladder via the new dispatch.env_mode helper
- add __all__ across extension modules, name the rotary registration records, and unify typing to the typing-module style
- rewrite test_linear_dispatch.py around behavioral routing assertions and document the M-banded policy in the developer docs
- Benchmark: L20 SM89, Python dispatch overhead 2.9us to 1.5us, auto now covers every projection shape at M in [2,4].

astrai/extension/__init__.py

@@ -1,18 +1,19 @@
-"""CUDA attention kernel wrappers with torch fallback.
+"""CUDA kernel wrappers, operator dispatch, and backend selection.
 
 Public API:
-    - ``attn_decode`` — single-query decode attention
-    - ``attn_prefill`` — multi-query prefill attention
-    - ``attn_paged_decode`` — paged decode attention (direct page-table access)
-    - ``AttentionBackend`` — ABC for attention computation strategies
-    - ``TorchNativeBackend`` — default SDPA backend with KV cache I/O
-    - ``CudaBackend`` — CUDA kernel backend with paged decode + prefill
+    - ``attention``, ``linear``, ``swiglu``, ``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_gemv`` / ``bf16_swiglu`` — directly callable linear/MLP kernels
+    - ``AttentionBackend`` / ``TorchNativeBackend`` / ``CudaBackend`` /
+      ``FlashAttnBackend`` — attention backend strategies
+    - ``resolve`` / ``explain`` / ``op_backend`` / ``env_mode`` — the shared
+      operator dispatcher (see ``astrai.extension.dispatch``)
 
 Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
-(blhd). Scale is always ``1/sqrt(head_dim)``.
-
-Each wrapper calls its compiled CUDA kernel directly. Fallback to torch
-SDPA is handled by the attention backend, not the wrapper functions.
+(blhd). Scale is always ``1/sqrt(head_dim)``. Wrapper functions call their
+compiled CUDA kernels directly; fallback is the backend's responsibility.
 """
 
 from astrai.extension.backend import (
@@ -36,6 +37,7 @@ from astrai.extension.dispatch import (
     Resolution,
     Spec,
     axis,
+    env_mode,
     explain,
     explain_plan,
     op_backend,
@@ -82,6 +84,7 @@ __all__ = [
     "Resolution",
     "Spec",
     "axis",
+    "env_mode",
     "explain",
     "explain_plan",
     "op_backend",