Layer norm tweaks (#482)

* Add some options to make layer-norm more configurable.

* Add the rms-norm variant.

* Replace the RmsNorm with the shared bits.

1 files changed, 9 insertions, 35 deletions

diff --git a/candle-wasm-examples/llama2-c/src/model.rs b/candle-wasm-examples/llama2-c/src/model.rs
index 3231cabf..d2b787ae 100644
--- a/candle-wasm-examples/llama2-c/src/model.rs
+++ b/candle-wasm-examples/llama2-c/src/model.rs
@@ -1,5 +1,5 @@
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
-use candle_nn::{Embedding, Linear, VarBuilder};
+use candle_nn::{rms_norm, Embedding, LayerNorm, Linear, VarBuilder};
 use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
 
@@ -71,32 +71,6 @@ fn embedding(cfg: &Config, vb: VarBuilder) -> Result<Embedding> {
     Ok(Embedding::new(embeddings, cfg.dim))
 }
 
-struct RmsNorm {
-    scale: Tensor,
-    eps: f64,
-}
-
-impl RmsNorm {
-    fn load(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
-        let scale = vb.get(size, "weight")?;
-        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,
@@ -239,14 +213,14 @@ 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,
@@ -267,9 +241,9 @@ 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,
@@ -282,12 +256,12 @@ impl Block {
 pub struct Llama {
     wte: Embedding,
     blocks: Vec<Block>,
-    ln_f: RmsNorm,
+    ln_f: LayerNorm,
     lm_head: Linear,
 }
 
 impl Llama {
-    fn new(wte: Embedding, blocks: Vec<Block>, ln_f: RmsNorm, lm_head: Linear) -> Self {
+    fn new(wte: Embedding, blocks: Vec<Block>, ln_f: LayerNorm, lm_head: Linear) -> Self {
         Self {
             wte,
             blocks,
@@ -311,7 +285,7 @@ impl Llama {
     pub fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
         let wte = embedding(cfg, vb.pp("model.embed_tokens"))?;
         let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
-        let norm = RmsNorm::load(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
+        let norm = 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();