Add training for the llama2.c example (#296)

* Rework the commands and run inference by default.

* Add the training module and load the training dataset.

* Random dataset iterator.

* Proper valid-loss computation.

* Compute the evaluation loss.

* Add more substance to the training loop.

candle-core/src/error.rs

@@ -228,3 +228,11 @@ macro_rules! bail {
         return Err($crate::Error::Msg(format!($fmt, $($arg)*).into()).bt())
     };
 }
+
+pub fn zip<T, U>(r1: Result<T>, r2: Result<U>) -> Result<(T, U)> {
+    match (r1, r2) {
+        (Ok(r1), Ok(r2)) => Ok((r1, r2)),
+        (Err(e), _) => Err(e),
+        (_, Err(e)) => Err(e),
+    }
+}

candle-core/src/lib.rs

@@ -44,7 +44,7 @@ mod device;
 pub mod display;
 mod dtype;
 mod dummy_cuda_backend;
-mod error;
+pub mod error;
 mod indexer;
 pub mod layout;
 #[cfg(feature = "mkl")]