Cudnn support (#445)

* Add a cudnn feature to be used for conv2d.

* Allocate the proper workspace.

* Only create a single cudnn handle per cuda device.

* Proper cudnn usage.

* Bugfix.

1 files changed, 4 insertions, 5 deletions

diff --git a/candle-core/examples/cuda_basics.rs b/candle-core/examples/cuda_basics.rs
index d902b9d5..12febb60 100644
--- a/candle-core/examples/cuda_basics.rs
+++ b/candle-core/examples/cuda_basics.rs
@@ -9,10 +9,9 @@ use candle_core::{Device, Tensor};
 
 fn main() -> Result<()> {
     let device = Device::new_cuda(0)?;
-    let t = Tensor::new(&[[1f32, 2., 3., 4.2]], &device)?;
-    let sum = t.sum_keepdim(0)?;
-    println!("{sum}");
-    let sum = t.sum_keepdim(1)?;
-    println!("{sum}");
+    let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?;
+    let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?;
+    let res = t.conv2d(&w, 1, 1)?;
+    println!("{res:?}");
     Ok(())
 }