Extract T5 module and add main function to use it (#829)

* Extract t5 out of musicgen

* Add main for t5 module

candle-examples/examples/t5/main.rs

@@ -0,0 +1,134 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use candle_transformers::models::t5;
+
+use anyhow::{anyhow, Error as E, Result};
+use candle::{DType, Tensor};
+use candle_nn::VarBuilder;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+const DTYPE: DType = DType::F32;
+const DEFAULT_PROMPT: &str = "Translate English to German: That is good.";
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Run offline (you must have the files already cached)
+    #[arg(long)]
+    offline: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: 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>,
+
+    /// Compute embeddings for this prompt or use the DEFAULT_PROMPT.
+    #[arg(long)]
+    prompt: Option<String>,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
+}
+
+impl Args {
+    fn build_model_and_tokenizer(&self) -> Result<(t5::T5EncoderModel, Tokenizer)> {
+        let device = candle_examples::device(self.cpu)?;
+        let default_model = "t5-small".to_string();
+        let default_revision = "refs/pr/15".to_string();
+        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
+            (Some(model_id), Some(revision)) => (model_id, revision),
+            (Some(model_id), None) => (model_id, "main".to_string()),
+            (None, Some(revision)) => (default_model, revision),
+            (None, None) => (default_model, default_revision),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let (config_filename, tokenizer_filename, weights_filename) = if self.offline {
+            let cache = Cache::default().repo(repo);
+            (
+                cache
+                    .get("config.json")
+                    .ok_or(anyhow!("Missing config file in cache"))?,
+                cache
+                    .get("tokenizer.json")
+                    .ok_or(anyhow!("Missing tokenizer file in cache"))?,
+                cache
+                    .get("model.safetensors")
+                    .ok_or(anyhow!("Missing weights file in cache"))?,
+            )
+        } else {
+            let api = Api::new()?;
+            let api = api.repo(repo);
+            (
+                api.get("config.json")?,
+                api.get("tokenizer.json")?,
+                api.get("model.safetensors")?,
+            )
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let config: t5::Config = serde_json::from_str(&config)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+        let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
+        let weights = weights.deserialize()?;
+        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
+        let model = t5::T5EncoderModel::load(vb, &config)?;
+        Ok((model, tokenizer))
+    }
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    let start = std::time::Instant::now();
+
+    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let device = &model.device;
+    let prompt = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    println!("Loaded and encoded {:?}", start.elapsed());
+    for idx in 0..args.n {
+        let start = std::time::Instant::now();
+        let ys = model.forward(&token_ids)?;
+        if idx == 0 {
+            println!("{ys}");
+        }
+        println!("Took {:?}", start.elapsed());
+    }
+    Ok(())
+}