Simple example fix.

candle-core/examples/llama/main.rs

@@ -13,6 +13,7 @@
 // transposition operations.
 use anyhow::{Error as E, Result};
 use clap::Parser;
+use rand::{distributions::Distribution, thread_rng};
 
 use candle::{DType, Device, Tensor};
 use candle_hub::{api::Api, Repo, RepoType};
@@ -137,10 +138,7 @@ impl Embedding {
     }
 
     fn forward(&self, indexes: &Tensor) -> Result<Tensor> {
-        Ok(Tensor::embedding(
-            indexes,
-            &self.embeddings.to_dtype(DType::F32)?,
-        )?)
+        Ok(Tensor::embedding(indexes, &self.embeddings)?)
     }
 }
 
@@ -354,7 +352,8 @@ impl Llama {
     fn forward(&self, x: &Tensor, freqs_cis: &Tensor) -> Result<Tensor> {
         // TODO: Support for mini-batches? (i.e. r2)
         let t = x.shape().r1()?;
-        let mut x = self.wte.forward(x)?;
+        let x = self.wte.forward(x)?;
+        let mut x = x.to_dtype(DType::F32)?;
         for block in self.blocks.iter() {
             x = block.forward(&x, freqs_cis)?;
         }
@@ -399,8 +398,8 @@ struct Args {
     npy: bool,
 
     /// The temperature used to generate samples.
-    #[arg(long, default_value_t = 1.0)]
-    temperature: f64,
+    #[arg(long)]
+    temperature: Option<f64>,
 
     /// The length of the sample to generate (in tokens).
     #[arg(long, default_value_t = 100)]
@@ -420,8 +419,34 @@ async fn main() -> Result<()> {
     };
     let api = Api::new()?;
     let repo = Repo::new("Narsil/amall-7b".to_string(), RepoType::Model);
-    let tokenizer_filename = api.get(&repo, "tokenizer.json").await?;
-    println!("Filename {tokenizer_filename:?}");
+    println!("building the model");
+    let config = Config::config_7b();
+    let cache = Cache::new(&device);
+    let start = std::time::Instant::now();
+    let (llama, tokenizer_filename) = if args.npy {
+        println!("building the model (NPY)");
+        (
+            Llama::load_npy(&device, "/data/llama.npz", &cache, &config)?,
+            std::path::Path::new("llama-tokenizer.json").to_path_buf(),
+        )
+    } else {
+        let tokenizer_filename = api.get(&repo, "tokenizer.json").await?;
+        let mut filenames = vec![];
+        for rfilename in [
+            "model-00001-of-00002.safetensors",
+            "model-00002-of-00002.safetensors",
+        ] {
+            let filename = api.get(&repo, rfilename).await?;
+            filenames.push(filename);
+        }
+
+        println!("building the model (SF)");
+        (
+            Llama::load(&device, &filenames, &cache, &config)?,
+            tokenizer_filename,
+        )
+    };
+    println!("Loaded in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
     let mut tokens = tokenizer
         .encode(START_PROMPT, true)
@@ -429,55 +454,39 @@ async fn main() -> Result<()> {
         .get_ids()
         .to_vec();
 
-    let mut filenames = vec![];
-    for rfilename in [
-        "model-00001-of-00002.safetensors",
-        "model-00002-of-00002.safetensors",
-    ] {
-        let filename = api.get(&repo, rfilename).await?;
-        filenames.push(filename);
-    }
-
-    println!("building the model");
-    let config = Config::config_7b();
-    let cache = Cache::new(&device);
-    let start = std::time::Instant::now();
-    let llama = if args.npy {
-        println!("building the model (NPY)");
-        Llama::load_npy(&device, &filenames, &cache, &config)?
-    } else {
-        println!("building the model (SF)");
-        Llama::load(&device, &filenames, &cache, &config)?
-    };
-    println!("Loaded in {:?}", start.elapsed());
-
     println!("pre-computing the positional embeddings");
     let freqs_cis = precompute_freqs_cis(&config, &device)?;
     println!("starting the inference loop");
     let mut new_tokens = vec![];
-    //let mut rng = thread_rng();
+    let mut rng = thread_rng();
     let start_gen = std::time::Instant::now();
     for index in 0..args.sample_len {
         let start_gen = std::time::Instant::now();
         let ctxt = &tokens[tokens.len().saturating_sub(CONTEXT_SIZE)..];
         let input = Tensor::new(ctxt, &device)?;
         let logits = llama.forward(&input, &freqs_cis)?;
-        let prs = (&logits / args.temperature)?.softmax(logits.rank() - 1)?;
-        let logits_v: Vec<f32> = prs.to_vec1()?;
-        let next_token = logits_v
-            .iter()
-            .enumerate()
-            .fold((0, logits_v[0]), |(idx_max, val_max), (idx, val)| {
-                if &val_max > val {
-                    (idx_max, val_max)
-                } else {
-                    (idx, *val)
-                }
-            })
-            .0 as u32;
-        // let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
 
-        // let next_token = distr.sample(&mut rng) as u32;
+        let next_token = if let Some(temperature) = args.temperature {
+            println!("Sampling with temperature {temperature:?}");
+            let prs = (&logits / temperature)?.softmax(logits.rank() - 1)?;
+            let logits_v: Vec<f32> = prs.to_vec1()?;
+            let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
+
+            distr.sample(&mut rng) as u32
+        } else {
+            let logits_v: Vec<f32> = logits.to_vec1()?;
+            logits_v
+                .iter()
+                .enumerate()
+                .fold((0, logits_v[0]), |(idx_max, val_max), (idx, val)| {
+                    if &val_max > val {
+                        (idx_max, val_max)
+                    } else {
+                        (idx, *val)
+                    }
+                })
+                .0 as u32
+        };
         tokens.push(next_token);
         new_tokens.push(next_token);
         println!("> {:?}", start_gen.elapsed());

candle-core/examples/llama/var_store.rs

@@ -1,7 +1,6 @@
 use super::*;
 use candle::{DType, Device, Result, Shape, Tensor, WithDType};
 use std::collections::HashMap;
-use std::path::PathBuf;
 use std::sync::Arc;
 
 #[allow(dead_code)]
@@ -142,11 +141,11 @@ impl Block {
 impl Llama {
     pub fn load_npy(
         device: &Device,
-        _filenames: &[PathBuf],
+        filename: &str,
         cache: &Cache,
         config: &Config,
     ) -> Result<Self> {
-        let weight_path = std::path::Path::new("/data/llama.npz");
+        let weight_path = std::path::Path::new(filename);
         let weights = if weight_path.exists() {
             println!("loading weights from {weight_path:?}");
             let start_load = std::time::Instant::now();