Add the mistral example. (#984)

* Add the mistral example.

* Use the two model files.

* Adjust the dtype.

* Tweak the weight paths.

* Remove the end of text token.

* Get the mistral model to generate some text.

candle-transformers/src/models/mistral.rs

@@ -1,7 +1,6 @@
-#![allow(unused)]
-use crate::models::with_tracing::{linear_no_bias, Embedding as E, Linear};
+use crate::models::with_tracing::{linear_no_bias, Linear};
 /// Mistral LLM, https://github.com/mistralai/mistral-src
-use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle::{DType, Device, Module, Result, Tensor, D};
 use candle_nn::{Activation, VarBuilder};
 use std::sync::Arc;
 
@@ -99,7 +98,7 @@ impl RotaryEmbedding {
         k: &Tensor,
         seqlen_offset: usize,
     ) -> Result<(Tensor, Tensor)> {
-        let (b_sz, seq_len, h, n_embd) = q.dims4()?;
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
         let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
         let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
         let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
@@ -240,7 +239,7 @@ impl Attention {
 
         let attn_weights = match attention_mask {
             None => attn_weights,
-            Some(mask) => (attn_weights + mask)?,
+            Some(mask) => attn_weights.broadcast_add(mask)?,
         };
         let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
         let attn_output = attn_weights.matmul(&value_states)?;
@@ -290,7 +289,7 @@ impl DecoderLayer {
         let xs = (xs + residual)?;
         let residual = &xs;
         let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
-        Ok(xs)
+        residual + xs
     }
 }
 
@@ -300,22 +299,24 @@ pub struct Model {
     layers: Vec<DecoderLayer>,
     norm: RmsNorm,
     lm_head: Linear,
+    #[allow(unused)]
     sliding_window: usize,
     device: Device,
 }
 
 impl Model {
     pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_m = vb.pp("model");
         let embed_tokens =
-            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb.pp("embed_tokens"))?;
-        let rotary_emb = Arc::new(RotaryEmbedding::new(cfg, vb.device())?);
+            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
+        let rotary_emb = Arc::new(RotaryEmbedding::new(cfg, vb_m.device())?);
         let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
-        let vb_l = vb.pp("layers");
+        let vb_l = vb_m.pp("layers");
         for layer_idx in 0..cfg.num_hidden_layers {
             let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
             layers.push(layer)
         }
-        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("norm"))?;
+        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
         let lm_head = linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
         Ok(Self {
             embed_tokens,
@@ -359,6 +360,8 @@ impl Model {
         for layer in self.layers.iter_mut() {
             xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
         }
-        xs.apply(&self.norm)?.apply(&self.lm_head)
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm)?
+            .apply(&self.lm_head)
     }
 }