main.rs

#[cfg(feature = "mkl")]
extern crate intel_mkl_src;

#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};

use anyhow::{Error as E, Result};
use candle::Tensor;
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::{PaddingParams, Tokenizer};

#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// Run on CPU rather than on GPU.
    #[arg(long)]
    cpu: 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>,

    /// When set, compute embeddings for this prompt.
    #[arg(long)]
    prompt: Option<String>,

    /// Use the pytorch weights rather than the safetensors ones
    #[arg(long)]
    use_pth: bool,

    /// The number of times to run the prompt.
    #[arg(long, default_value = "1")]
    n: usize,

    /// L2 normalization for embeddings.
    #[arg(long, default_value = "true")]
    normalize_embeddings: bool,

    /// Use tanh based approximation for Gelu instead of erf implementation.
    #[arg(long, default_value = "false")]
    approximate_gelu: bool,

    /// Include padding token embeddings when performing mean pooling. By default, these are masked away.
    #[arg(long, default_value = "false")]
    include_padding_embeddings: bool,
}

impl Args {
    fn build_model_and_tokenizer(&self) -> Result<(BertModel, Tokenizer)> {
        let device = candle_examples::device(self.cpu)?;
        let default_model = "sentence-transformers/all-MiniLM-L6-v2".to_string();
        let default_revision = "refs/pr/21".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) = {
            let api = Api::new()?;
            let api = api.repo(repo);
            let config = api.get("config.json")?;
            let tokenizer = api.get("tokenizer.json")?;
            let weights = if self.use_pth {
                api.get("pytorch_model.bin")?
            } else {
                api.get("model.safetensors")?
            };
            (config, tokenizer, weights)
        };
        let config = std::fs::read_to_string(config_filename)?;
        let mut config: Config = serde_json::from_str(&config)?;
        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;

        let vb = if self.use_pth {
            VarBuilder::from_pth(&weights_filename, DTYPE, &device)?
        } else {
            unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
        };
        if self.approximate_gelu {
            config.hidden_act = HiddenAct::GeluApproximate;
        }
        let model = BertModel::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;

    if let Some(prompt) = args.prompt {
        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)?;
        let token_type_ids = token_ids.zeros_like()?;
        println!("Loaded and encoded {:?}", start.elapsed());
        for idx in 0..args.n {
            let start = std::time::Instant::now();
            let ys = model.forward(&token_ids, &token_type_ids, None)?;
            if idx == 0 {
                println!("{ys}");
            }
            println!("Took {:?}", start.elapsed());
        }
    } else {
        let sentences = [
            "The cat sits outside",
            "A man is playing guitar",
            "I love pasta",
            "The new movie is awesome",
            "The cat plays in the garden",
            "A woman watches TV",
            "The new movie is so great",
            "Do you like pizza?",
        ];
        let n_sentences = sentences.len();
        if let Some(pp) = tokenizer.get_padding_mut() {
            pp.strategy = tokenizers::PaddingStrategy::BatchLongest
        } else {
            let pp = PaddingParams {
                strategy: tokenizers::PaddingStrategy::BatchLongest,
                ..Default::default()
            };
            tokenizer.with_padding(Some(pp));
        }
        let tokens = tokenizer
            .encode_batch(sentences.to_vec(), true)
            .map_err(E::msg)?;
        let token_ids = tokens
            .iter()
            .map(|tokens| {
                let tokens = tokens.get_ids().to_vec();
                Ok(Tensor::new(tokens.as_slice(), device)?)
            })
            .collect::<Result<Vec<_>>>()?;
        let attention_mask = tokens
            .iter()
            .map(|tokens| {
                let tokens = tokens.get_attention_mask().to_vec();
                Ok(Tensor::new(tokens.as_slice(), device)?)
            })
            .collect::<Result<Vec<_>>>()?;

        let token_ids = Tensor::stack(&token_ids, 0)?;
        let attention_mask = Tensor::stack(&attention_mask, 0)?;
        let token_type_ids = token_ids.zeros_like()?;
        println!("running inference on batch {:?}", token_ids.shape());
        let embeddings = model.forward(&token_ids, &token_type_ids, Some(&attention_mask))?;
        println!("generated embeddings {:?}", embeddings.shape());
        let embeddings = if args.include_padding_embeddings {
            // Apply avg-pooling by taking the mean embedding value for all
            // tokens, including padding. This was the original behavior of this
            // example, and we'd like to preserve it for posterity.
            let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
            (embeddings.sum(1)? / (n_tokens as f64))?
        } else {
            // Apply avg-pooling by taking the mean embedding value for all
            // tokens (after applying the attention mask from tokenization).
            // This should produce the same numeric result as the
            // `sentence_transformers` Python library.
            let attention_mask_for_pooling = attention_mask.to_dtype(DTYPE)?.unsqueeze(2)?;
            let sum_mask = attention_mask_for_pooling.sum(1)?;
            let embeddings = (embeddings.broadcast_mul(&attention_mask_for_pooling)?).sum(1)?;
            embeddings.broadcast_div(&sum_mask)?
        };
        let embeddings = if args.normalize_embeddings {
            normalize_l2(&embeddings)?
        } else {
            embeddings
        };
        println!("pooled embeddings {:?}", embeddings.shape());

        let mut similarities = vec![];
        for i in 0..n_sentences {
            let e_i = embeddings.get(i)?;
            for j in (i + 1)..n_sentences {
                let e_j = embeddings.get(j)?;
                let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
                let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
                let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
                let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
                similarities.push((cosine_similarity, i, j))
            }
        }
        similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
        for &(score, i, j) in similarities[..5].iter() {
            println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
        }
    }
    Ok(())
}

pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
}