Finished scaffolding, lots of TODOs

- Most kernels just copy themselfs to get the shapes correct
- Matmul works only in 1 case and simply empty allocates otherwise
- Logits and randomized to make the demo finish itself.

Performance is quite bad (30ms/token), but lot's of prints and allocs and some actual sending to metal.

Couln't get it super high by removing the obvious blockers (println + the actual running matmuls).

Allocations takes between 1us and 100us and seems very stable, Maybe metal doesn't really have a smart allocator and we'll need to own it.

candle-core/src/op.rs

@@ -182,7 +182,7 @@ pub trait CustomOp1 {
         _layout: &Layout,
     ) -> Result<(MetalStorage, Shape)> {
         Err(crate::Error::Metal(
-            format!("no cuda implementation for {}", self.name()).into(),
+            format!("no metal implementation for {}", self.name()).into(),
         ))
     }