perf: accelerate decode linear with bf16 gemv

- add decode-shape benchmark harness
- add bf16 GEMV CUDA primitive with head-dim generic kernel
- dispatch decode-time linear layers to gemv for M=1
- extend gemv coverage to small decode batches

astrai/model/components/linear.py

@@ -1,8 +1,9 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from torch import Tensor
 
+from astrai.extension.backend.linear import linear
+
 
 class Linear(nn.Module):
     def __init__(
@@ -21,4 +22,4 @@ class Linear(nn.Module):
             nn.init.uniform_(self.bias, -bound, bound)
 
     def forward(self, x: Tensor) -> Tensor:
-        return F.linear(x, self.weight, self.bias)
+        return linear(x, self.weight, self.bias)