Add bert model

README.md

@@ -35,6 +35,7 @@ converted:
 - `llama`
 - `mistral`
 - `falcon`
+- `bert`
 
 ```rust
 use candle_core::{DType, Device, Module, Result, Tensor};

candle-lora-transformers/examples/bert.rs

@@ -0,0 +1,223 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use candle_lora::LoraConfig;
+use candle_lora_transformers::bert::{BertModel, Config, DTYPE};
+
+use anyhow::{Error as E, Result};
+use candle_core::{Tensor, Var};
+use candle_nn::{VarBuilder, VarMap};
+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,
+}
+
+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 config: Config = serde_json::from_str(&config)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+        let map = VarMap::new();
+        if self.use_pth {
+            let mut ws = map.data().lock().unwrap();
+
+            let tensors = candle_core::pickle::PthTensors::new(&weights_filename)?;
+            for (name, _) in tensors.tensor_infos() {
+                let tensor = tensors
+                    .get(&name)?
+                    .expect("Tensor not found")
+                    .to_device(&device)?
+                    .to_dtype(DTYPE)?;
+                ws.insert(name.to_string(), Var::from_tensor(&tensor)?);
+            }
+        } else {
+            let mut ws = map.data().lock().unwrap();
+
+            let tensors =
+                unsafe { candle_core::safetensors::MmapedSafetensors::multi(&[weights_filename])? };
+            for (name, _) in tensors.tensors() {
+                let tensor = tensors
+                    .load(&name, &device)?
+                    .to_device(&device)?
+                    .to_dtype(DTYPE)?;
+                ws.insert(name, Var::from_tensor(&tensor)?);
+            }
+        };
+
+        let vb = VarBuilder::from_varmap(&map, DTYPE, &device);
+
+        let loraconfig = LoraConfig::new(1, 1., None);
+        let model = BertModel::load(vb, &config, true, loraconfig)?;
+        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)?;
+            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 token_ids = Tensor::stack(&token_ids, 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)?;
+        println!("generated embeddings {:?}", embeddings.shape());
+        // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
+        let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+        let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+        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()?)?)
+}