T5 quantized example (#922)

* Load gguf files for the quantized t5.

* Add the quantized t5 example.

* Allow for loading local files.

* Add some support for quantizing safetensor files.

* Transpose before quantizing.

* Quantized t5.

* Retrieve the weights from the hub.

candle-examples/examples/quantized-t5/main.rs

@@ -0,0 +1,186 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use std::io::Write;
+use std::path::PathBuf;
+
+use candle_transformers::models::quantized_t5 as t5;
+
+use anyhow::{Error as E, Result};
+use candle::{Device, Tensor};
+use candle_transformers::generation::LogitsProcessor;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+#[derive(Parser, Debug, Clone)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// The model repository to use on the HuggingFace hub.
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    weight_file: Option<String>,
+
+    // Enable/disable decoding.
+    #[arg(long, default_value = "false")]
+    disable_cache: bool,
+
+    /// Use this prompt, otherwise compute sentence similarities.
+    #[arg(long)]
+    prompt: String,
+
+    /// The temperature used to generate samples.
+    #[arg(long, default_value_t = 0.8)]
+    temperature: f64,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+}
+
+struct T5ModelBuilder {
+    device: Device,
+    config: t5::Config,
+    weights_filename: PathBuf,
+}
+
+impl T5ModelBuilder {
+    pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
+        let device = Device::Cpu;
+        let default_model = "lmz/candle-quantized-t5".to_string();
+        let (model_id, revision) = match (args.model_id.to_owned(), args.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, "main".to_string()),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let api = Api::new()?;
+        let api = api.repo(repo);
+        let config_filename = api.get("config.json")?;
+        let tokenizer_filename = api.get("tokenizer.json")?;
+        let weights_filename = match &args.weight_file {
+            Some(filename) => std::path::PathBuf::from(filename),
+            None => api.get("model.gguf")?,
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let mut config: t5::Config = serde_json::from_str(&config)?;
+        config.use_cache = !args.disable_cache;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+        Ok((
+            Self {
+                device,
+                config,
+                weights_filename,
+            },
+            tokenizer,
+        ))
+    }
+
+    pub fn build_model(&self) -> Result<t5::T5ForConditionalGeneration> {
+        let vb = t5::VarBuilder::from_gguf(&self.weights_filename)?;
+        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
+    }
+}
+
+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 (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
+    let device = &builder.device;
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(args.prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    let mut model = builder.build_model()?;
+    let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
+    let temperature = if args.temperature <= 0. {
+        None
+    } else {
+        Some(args.temperature)
+    };
+    let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
+    let encoder_output = model.encode(&input_token_ids)?;
+    let start = std::time::Instant::now();
+
+    for index in 0.. {
+        if output_token_ids.len() > 512 {
+            break;
+        }
+        let decoder_token_ids = if index == 0 || !builder.config.use_cache {
+            Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
+        } else {
+            let last_token = *output_token_ids.last().unwrap();
+            Tensor::new(&[last_token], device)?.unsqueeze(0)?
+        };
+        let logits = model
+            .decode(&decoder_token_ids, &encoder_output)?
+            .squeeze(0)?;
+        let logits = if args.repeat_penalty == 1. {
+            logits
+        } else {
+            let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
+            candle_transformers::utils::apply_repeat_penalty(
+                &logits,
+                args.repeat_penalty,
+                &output_token_ids[start_at..],
+            )?
+        };
+
+        let next_token_id = logits_processor.sample(&logits)?;
+        if next_token_id as usize == builder.config.eos_token_id {
+            break;
+        }
+        output_token_ids.push(next_token_id);
+        if let Some(text) = tokenizer.id_to_token(next_token_id) {
+            let text = text.replace('▁', " ").replace("<0x0A>", "\n");
+            print!("{text}");
+            std::io::stdout().flush()?;
+        }
+    }
+    let dt = start.elapsed();
+    println!(
+        "\n{} tokens generated ({:.2} token/s)\n",
+        output_token_ids.len(),
+        output_token_ids.len() as f64 / dt.as_secs_f64(),
+    );
+    Ok(())
+}