From d8f75ceeaa4702b641a9f71ec348fc54a32f4cd7 Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Wed, 5 Jul 2023 07:41:14 +0000
Subject: [PATCH] Some polish.

---
 candle-examples/examples/bert/main.rs | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/candle-examples/examples/bert/main.rs b/candle-examples/examples/bert/main.rs
index 9c9dc206..4de0aeac 100644
--- a/candle-examples/examples/bert/main.rs
+++ b/candle-examples/examples/bert/main.rs
@@ -621,18 +621,26 @@ struct Args {
     #[arg(long)]
     offline: bool,
 
+    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
     #[arg(long)]
     model_id: Option<String>,
 
     #[arg(long)]
     revision: Option<String>,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "This is an example sentence")]
+    prompt: String,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
 }
 
 #[tokio::main]
 async fn main() -> Result<()> {
     use tokenizers::Tokenizer;
     let start = std::time::Instant::now();
-    println!("Building {:?}", start.elapsed());
 
     let args = Args::parse();
     let device = if args.cpu {
@@ -672,29 +680,25 @@ async fn main() -> Result<()> {
             api.get(&repo, "model.safetensors").await?,
         )
     };
-    println!("Building {:?}", start.elapsed());
     let config = std::fs::read_to_string(config_filename)?;
     let config: Config = serde_json::from_str(&config)?;
-    println!("Config loaded {:?}", start.elapsed());
     let mut tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
     let tokenizer = tokenizer.with_padding(None).with_truncation(None);
-    println!("Tokenizer loaded {:?}", start.elapsed());
 
     let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
     let weights = weights.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, device.clone());
     let model = BertModel::load(&vb, &config)?;
-    println!("Loaded {:?}", start.elapsed());
 
     let tokens = tokenizer
-        .encode("This is an example sentence", true)
+        .encode(args.prompt, true)
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
     let token_ids = Tensor::new(&tokens[..], &device)?.unsqueeze(0)?;
     let token_type_ids = token_ids.zeros_like()?;
     println!("Loaded and encoded {:?}", start.elapsed());
-    for _ in 0..100 {
+    for _ in 0..args.n {
         let start = std::time::Instant::now();
         let _ys = model.forward(&token_ids, &token_type_ids)?;
         println!("Took {:?}", start.elapsed());