1 files changed, 8 insertions, 34 deletions

diff --git a/candle-examples/examples/llama2-c/model.rs b/candle-examples/examples/llama2-c/model.rs
index 77900d27..75269665 100644
--- a/candle-examples/examples/llama2-c/model.rs
+++ b/candle-examples/examples/llama2-c/model.rs
@@ -1,6 +1,6 @@
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
 use candle_nn::linear_no_bias as linear;
-use candle_nn::{embedding, Embedding, Linear, VarBuilder};
+use candle_nn::{embedding, rms_norm, Embedding, LayerNorm, Linear, VarBuilder};
 use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
 
@@ -94,32 +94,6 @@ fn silu(xs: &Tensor) -> Result<Tensor> {
     xs / (xs.neg()?.exp()? + 1.0)?
 }
 
-struct RmsNorm {
-    scale: Tensor,
-    eps: f64,
-}
-
-impl RmsNorm {
-    fn load(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
-        let scale = vb.get_or_init(size, "weight", candle_nn::Init::Const(1.))?;
-        Ok(Self { scale, eps })
-    }
-
-    fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let (b_sz, seq_len, hidden_size) = x.dims3()?;
-        let norm_x = (x.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
-        let norm_x = norm_x.broadcast_as((b_sz, seq_len, hidden_size))?;
-        let x_normed = (x / (norm_x + self.eps)?.sqrt()?)?;
-        let size = self.scale.dims1()?;
-        let scale = self
-            .scale
-            .to_dtype(DType::F32)?
-            .broadcast_as((b_sz, seq_len, size))?;
-        let x = (scale * x_normed)?;
-        Ok(x)
-    }
-}
-
 struct CausalSelfAttention {
     q_proj: Linear,
     k_proj: Linear,
@@ -262,14 +236,14 @@ impl Mlp {
 }
 
 struct Block {
-    rms_1: RmsNorm,
+    rms_1: LayerNorm,
     attn: CausalSelfAttention,
-    rms_2: RmsNorm,
+    rms_2: LayerNorm,
     mlp: Mlp,
 }
 
 impl Block {
-    fn new(rms_1: RmsNorm, attn: CausalSelfAttention, rms_2: RmsNorm, mlp: Mlp) -> Self {
+    fn new(rms_1: LayerNorm, attn: CausalSelfAttention, rms_2: LayerNorm, mlp: Mlp) -> Self {
         Self {
             rms_1,
             attn,
@@ -290,9 +264,9 @@ impl Block {
     fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
         let attn = CausalSelfAttention::load(vb.pp("self_attn"), cache, cfg)?;
         let mlp = Mlp::load(vb.pp("mlp"), cfg)?;
-        let input_layernorm = RmsNorm::load(cfg.dim, cfg.norm_eps, vb.pp("input_layernorm"))?;
+        let input_layernorm = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("input_layernorm"))?;
         let post_attention_layernorm =
-            RmsNorm::load(cfg.dim, cfg.norm_eps, vb.pp("post_attention_layernorm"))?;
+            rms_norm(cfg.dim, cfg.norm_eps, vb.pp("post_attention_layernorm"))?;
         Ok(Self::new(
             input_layernorm,
             attn,
@@ -305,7 +279,7 @@ impl Block {
 pub struct Llama {
     wte: Embedding,
     blocks: Vec<Block>,
-    ln_f: RmsNorm,
+    ln_f: LayerNorm,
     lm_head: Linear,
     pub config: Config,
 }
@@ -325,7 +299,7 @@ impl Llama {
     pub fn load(vb: VarBuilder, cache: &Cache, cfg: Config) -> Result<Self> {
         let wte = embedding(cfg.vocab_size, cfg.dim, vb.pp("model.embed_tokens"))?;
         let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
-        let ln_f = RmsNorm::load(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
+        let ln_f = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
         let blocks: Vec<_> = (0..cfg.n_layers)
             .map(|i| Block::load(vb.pp(&format!("model.layers.{i}")), cache, &cfg).unwrap())
             .collect();