Add the AdamW optimizer. (#307)

* Add the AdamW optimizer.

* Add some AdamW test validated against PyTorch.

candle-nn/tests/test_utils.rs

@@ -0,0 +1,39 @@
+#![allow(dead_code)]
+use candle::{Result, Tensor};
+
+pub fn to_vec0_round(t: &Tensor, digits: i32) -> Result<f32> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec0::<f32>()?;
+    Ok(f32::round(t * b) / b)
+}
+
+pub fn to_vec1_round(t: &Tensor, digits: i32) -> Result<Vec<f32>> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec1::<f32>()?;
+    let t = t.iter().map(|t| f32::round(t * b) / b).collect();
+    Ok(t)
+}
+
+pub fn to_vec2_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec2::<f32>()?;
+    let t = t
+        .iter()
+        .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
+        .collect();
+    Ok(t)
+}
+
+pub fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec3::<f32>()?;
+    let t = t
+        .iter()
+        .map(|t| {
+            t.iter()
+                .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
+                .collect()
+        })
+        .collect();
+    Ok(t)
+}