Sketch the candle-nn crate. (#115)

* Sketch the candle-nn crate.

* Tweak the cuda dependencies.

* More cuda tweaks.

candle-nn/src/layer_norm.rs

@@ -0,0 +1,34 @@
+use candle::{DType, Result, Tensor};
+
+// This layer norm version handles both weight and bias so removes the mean.
+#[derive(Debug)]
+pub struct LayerNorm {
+    weight: Tensor,
+    bias: Tensor,
+    eps: f64,
+}
+
+impl LayerNorm {
+    pub fn new(weight: Tensor, bias: Tensor, eps: f64) -> Self {
+        Self { weight, bias, eps }
+    }
+
+    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x_dtype = x.dtype();
+        let internal_dtype = match x_dtype {
+            DType::F16 | DType::BF16 => DType::F32,
+            d => d,
+        };
+        let (_bsize, _seq_len, hidden_size) = x.shape().r3()?;
+        let x = x.to_dtype(internal_dtype)?;
+        let mean_x = (x.sum(&[2])? / hidden_size as f64)?;
+        let x = x.broadcast_sub(&mean_x)?;
+        let norm_x = ((&x * &x)?.sum(&[2])? / hidden_size as f64)?;
+        let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
+        let x = x_normed
+            .to_dtype(x_dtype)?
+            .broadcast_mul(&self.weight)?
+            .broadcast_add(&self.bias)?;
+        Ok(x)
+    }
+}