Add BERT family of models

README.md

@@ -5,17 +5,18 @@ examples constructed using the [Burn](https://github.com/burn-rs/burn) deep lear
 
 ## Collection of Official Models
 
-| Model                                          | Description                                       | Repository Link                              |
-| ---------------------------------------------- | ------------------------------------------------- | -------------------------------------------- |
-| [SqueezeNet](https://arxiv.org/abs/1602.07360) | A small CNN-based model for image classification. | [squeezenet-burn](squeezenet-burn/README.md) |
-| [ResNet](https://arxiv.org/abs/1512.03385)     | A CNN based on residual blocks with skip connections. | [resnet-burn](resnet-burn/README.md) |
+| Model                                          | Description                                           | Repository Link                              |
+|------------------------------------------------|-------------------------------------------------------|----------------------------------------------|
+| [SqueezeNet](https://arxiv.org/abs/1602.07360) | A small CNN-based model for image classification.     | [squeezenet-burn](squeezenet-burn/README.md) |
+| [ResNet](https://arxiv.org/abs/1512.03385)     | A CNN based on residual blocks with skip connections. | [resnet-burn](resnet-burn/README.md)         |
+| [RoBERTa](https://arxiv.org/abs/1907.11692)    | A robustly optimized BERT pretraining approach.       | [bert-burn](bert-burn/README.md)             |
 
 ## Community Contributions
 
 Explore the curated list of models developed by the community ♥.
 
 | Model                                       | Description                                                       | Repository Link                                                                   |
-| ------------------------------------------- | ----------------------------------------------------------------- | --------------------------------------------------------------------------------- |
+|---------------------------------------------|-------------------------------------------------------------------|-----------------------------------------------------------------------------------|
 | [Llama 2](https://arxiv.org/abs/2307.09288) | LLMs by Meta AI, ranging from 7 billion to 70 billion parameters. | [Gadersd/llama2-burn](https://github.com/Gadersd/llama2-burn)                     |
 | [Whisper](https://arxiv.org/abs/2212.04356) | A general-purpose speech recognition model by OpenAI.             | [Gadersd/whisper-burn](https://github.com/Gadersd/whisper-burn)                   |
 | Stable Diffusion v1.4                       | An image generation model developed by Stability AI.              | [Gadersd/stable-diffusion-burn](https://github.com/Gadersd/stable-diffusion-burn) |

bert-burn/Cargo.toml

@@ -0,0 +1,37 @@
+[package]
+authors = ["Aasheesh Singh [email protected]"]
+license = "MIT OR Apache-2.0"
+name = "bert-burn"
+version="0.1.0"
+edition = "2021"
+
+[features]
+default = ["burn/dataset"]
+f16 = []
+ndarray = ["burn/ndarray"]
+tch-cpu = ["burn/tch"]
+tch-gpu = ["burn/tch"]
+wgpu = ["burn/wgpu"]
+fusion = ["burn/fusion"]
+# To be replaced by burn-safetensors once supported: https://github.com/tracel-ai/burn/issues/626
+safetensors = ["candle-core/default"]
+
+
+[dependencies]
+# Burn
+burn = {version = "0.12.1", default-features = false}
+candle-core = { version = "0.3.2", optional = true}
+# Tokenizer
+tokenizers = { version = "0.15.0", default-features = false, features = [
+  "onig",
+  "http",
+] }
+burn-import = "0.12.1"
+derive-new = "0.6.0"
+hf-hub = { version = "0.3.2", features = ["tokio"] }
+
+# Utils
+serde = { version = "1.0.196", features = ["std", "derive"] }
+libm = "0.2.8"
+serde_json = "1.0.113"
+tokio = "1.35.1"

bert-burn/LICENSE-APACHE

@@ -0,0 +1 @@
+../LICENSE-APACHE

bert-burn/LICENSE-MIT

@@ -0,0 +1 @@
+../LICENSE-MIT

bert-burn/README.md

@@ -0,0 +1,39 @@
+# Bert-Burn Model
+
+This project provides an example implementation for inference on the BERT family of models. The following compatible
+bert-variants: `roberta-base`(**default**)/`roberta-large`, `bert-base-uncased`/`bert-large-uncased`/`bert-base-cased`/`bert-large-cased`
+can be loaded as following. The pre-trained weights and config files are automatically downloaded
+from: [HuggingFace Model hub](https://huggingface.co/FacebookAI/roberta-base/tree/main)
+
+### To include the model in your project
+
+Add this to your `Cargo.toml`:
+
+```toml
+[dependencies]
+bert-burn = { git = "https://github.com/burn-rs/models", package = "bert-burn", default-features = false }
+```
+
+## Example Usage
+
+Example usage for getting sentence embedding from given input text. The model supports multiple backends from burn
+(e.g. `ndarray`, `wgpu`, `tch-gpu`, `tch-cpu`) which can be selected using the `--features` flag. An example with `wgpu`
+backend is shown below. The `fusion` flag is used to enable kernel fusion for the `wgpu` backend. It is not required
+with other backends. The `safetensors` flag is used to support loading weights in `safetensors` format via `candle-core`
+crate.
+
+### WGPU backend
+
+```bash
+cd bert-burn/
+# Get sentence embeddings from the RobBERTa encoder (default)
+cargo run --example infer-embedding --release --features wgpu,fusion,safetensors
+
+# Using bert-base-uncased model
+cargo run --example infer-embedding --release --features wgpu,fusion,safetensors bert-base-uncased 
+
+# Using roberta-large model
+cargo run --example infer-embedding --release --features wgpu,fusion,safetensors roberta-large
+```
+
+

bert-burn/examples/infer-embedding.rs

@@ -0,0 +1,154 @@
+use bert_burn::data::{BertInputBatcher, BertTokenizer};
+use bert_burn::loader::{download_hf_model, load_model_config};
+use bert_burn::model::BertModel;
+use burn::data::dataloader::batcher::Batcher;
+use burn::tensor::backend::Backend;
+use burn::tensor::Tensor;
+use std::env;
+use std::sync::Arc;
+
+#[cfg(not(feature = "f16"))]
+#[allow(dead_code)]
+type ElemType = f32;
+#[cfg(feature = "f16")]
+type ElemType = burn::tensor::f16;
+
+pub fn launch<B: Backend>(device: B::Device) {
+    let args: Vec<String> = env::args().collect();
+    let default_model = "roberta-base".to_string();
+    let model_variant = if args.len() > 1 {
+        // Use the argument provided by the user
+        // Possible values: "bert-base-uncased", "roberta-large" etc.
+        &args[1]
+    } else {
+        // Use the default value if no argument is provided
+        &default_model
+    };
+
+    println!("Model variant: {}", model_variant);
+
+    let text_samples = vec![
+        "Jays power up to take finale Contrary to popular belief, the power never really \
+                 snapped back at SkyDome on Sunday. The lights came on after an hour delay, but it \
+                 took some extra time for the batting orders to provide some extra wattage."
+            .to_string(),
+        "Yemen Sentences 15 Militants on Terror Charges A court in Yemen has sentenced one \
+                 man to death and 14 others to prison terms for a series of attacks and terrorist \
+                 plots in 2002, including the bombing of a French oil tanker."
+            .to_string(),
+        "IBM puts grids to work at U.S. Open IBM will put a collection of its On \
+                 Demand-related products and technologies to this test next week at the U.S. Open \
+                 tennis championships, implementing a grid-based infrastructure capable of running \
+                 multiple workloads including two not associated with the tournament."
+            .to_string(),
+    ];
+
+    let (config_file, model_file) = download_hf_model(model_variant);
+    let model_config = load_model_config(config_file);
+
+    let model: BertModel<B> =
+        BertModel::from_safetensors(model_file, &device, model_config.clone());
+
+    let tokenizer = Arc::new(BertTokenizer::new(
+        model_variant.to_string(),
+        model_config.pad_token_id.clone(),
+    ));
+
+    // Batch the input samples to max sequence length with padding
+    let batcher = Arc::new(BertInputBatcher::<B>::new(
+        tokenizer.clone(),
+        device.clone(),
+        model_config.max_seq_len.unwrap().clone(),
+    ));
+
+    // Batch input samples using the batcher Shape: [Batch size, Seq_len]
+    let input = batcher.batch(text_samples.clone());
+    let [batch_size, _seq_len] = input.tokens.dims();
+    println!("Input: {:?} // (Batch Size, Seq_len)", input.tokens.shape());
+
+    let output = model.forward(input);
+
+    // get sentence embedding from the first [CLS] token
+    let cls_token_idx = 0;
+
+    // Embedding size
+    let d_model = model_config.hidden_size.clone();
+    let sentence_embedding =
+        output
+            .clone()
+            .slice([0..batch_size, cls_token_idx..cls_token_idx + 1, 0..d_model]);
+
+    let sentence_embedding: Tensor<B, 2> = sentence_embedding.squeeze(1);
+    println!(
+        "Roberta Sentence embedding {:?} // (Batch Size, Embedding_dim)",
+        sentence_embedding.shape()
+    );
+}
+
+#[cfg(any(
+    feature = "ndarray",
+    feature = "ndarray-blas-netlib",
+    feature = "ndarray-blas-openblas",
+    feature = "ndarray-blas-accelerate",
+))]
+mod ndarray {
+    use burn::backend::ndarray::{NdArray, NdArrayDevice};
+
+    use crate::{launch, ElemType};
+
+    pub fn run() {
+        launch::<NdArray<ElemType>>(NdArrayDevice::Cpu);
+    }
+}
+
+#[cfg(feature = "tch-gpu")]
+mod tch_gpu {
+    use crate::{launch, ElemType};
+    use burn::backend::libtorch::{LibTorch, LibTorchDevice};
+
+    pub fn run() {
+        #[cfg(not(target_os = "macos"))]
+        let device = LibTorchDevice::Cuda(0);
+        #[cfg(target_os = "macos")]
+        let device = LibTorchDevice::Mps;
+
+        launch::<LibTorch<ElemType>>(device);
+    }
+}
+
+#[cfg(feature = "tch-cpu")]
+mod tch_cpu {
+    use crate::{launch, ElemType};
+    use burn::backend::libtorch::{LibTorch, LibTorchDevice};
+
+    pub fn run() {
+        launch::<LibTorch<ElemType>>(LibTorchDevice::Cpu);
+    }
+}
+
+#[cfg(feature = "wgpu")]
+mod wgpu {
+    use crate::{launch, ElemType};
+    use burn::backend::wgpu::{AutoGraphicsApi, Wgpu, WgpuDevice};
+    use burn::backend::Fusion;
+
+    pub fn run() {
+        launch::<Fusion<Wgpu<AutoGraphicsApi, ElemType, i32>>>(WgpuDevice::default());
+    }
+}
+
+fn main() {
+    #[cfg(any(
+        feature = "ndarray",
+        feature = "ndarray-blas-netlib",
+        feature = "ndarray-blas-openblas",
+        feature = "ndarray-blas-accelerate",
+    ))]
+    ndarray::run();
+    #[cfg(feature = "tch-gpu")]
+    tch_gpu::run();
+    #[cfg(feature = "tch-cpu")]
+    tch_cpu::run();
+    #[cfg(feature = "wgpu")]
+    wgpu::run();
+}

bert-burn/src/data/batcher.rs

@@ -0,0 +1,51 @@
+use super::tokenizer::Tokenizer;
+use burn::{
+    data::dataloader::batcher::Batcher,
+    nn::attention::generate_padding_mask,
+    tensor::{backend::Backend, Bool, Int, Tensor},
+};
+use std::sync::Arc;
+
+#[derive(new)]
+pub struct BertInputBatcher<B: Backend> {
+    /// Tokenizer for converting input text string to token IDs
+    tokenizer: Arc<dyn Tokenizer>,
+    /// Device on which to perform computation (e.g., CPU or CUDA device)
+    device: B::Device,
+    /// Maximum sequence length for tokenized text
+    max_seq_length: usize,
+}
+
+#[derive(Debug, Clone, new)]
+pub struct BertInferenceBatch<B: Backend> {
+    /// Tokenized text as 2D tensor: [batch_size, max_seq_length]
+    pub tokens: Tensor<B, 2, Int>,
+    /// Padding mask for the tokenized text containing booleans for padding locations
+    pub mask_pad: Tensor<B, 2, Bool>,
+}
+
+impl<B: Backend> Batcher<String, BertInferenceBatch<B>> for BertInputBatcher<B> {
+    /// Batches a vector of strings into an inference batch
+    fn batch(&self, items: Vec<String>) -> BertInferenceBatch<B> {
+        let mut tokens_list = Vec::with_capacity(items.len());
+
+        // Tokenize each string
+        for item in items {
+            tokens_list.push(self.tokenizer.encode(&item));
+        }
+
+        // Generate padding mask for tokenized text
+        let mask = generate_padding_mask(
+            self.tokenizer.pad_token(),
+            tokens_list,
+            Some(self.max_seq_length),
+            &self.device,
+        );
+
+        // Create and return inference batch
+        BertInferenceBatch {
+            tokens: mask.tensor,
+            mask_pad: mask.mask,
+        }
+    }
+}

bert-burn/src/data/mod.rs

@@ -0,0 +1,5 @@
+mod batcher;
+mod tokenizer;
+
+pub use batcher::*;
+pub use tokenizer::*;

bert-burn/src/data/tokenizer.rs

@@ -0,0 +1,64 @@
+pub trait Tokenizer: Send + Sync {
+    /// Converts a text string into a sequence of tokens.
+    fn encode(&self, value: &str) -> Vec<usize>;
+
+    /// Converts a sequence of tokens back into a text string.
+    fn decode(&self, tokens: &[usize]) -> String;
+
+    /// Gets the size of the tokenizer's vocabulary.
+    fn vocab_size(&self) -> usize;
+
+    /// Gets the token used for padding sequences to a consistent length.
+    fn pad_token(&self) -> usize;
+
+    /// Gets the string representation of the padding token.
+    /// The default implementation uses `decode` on the padding token.
+    fn pad_token_value(&self) -> String {
+        self.decode(&[self.pad_token()])
+    }
+}
+
+/// Struct represents a specific tokenizer using the Roberta BPE tokenization strategy.
+pub struct BertTokenizer {
+    // The underlying tokenizer from the `tokenizers` library.
+    tokenizer: tokenizers::Tokenizer,
+    pad_token: usize,
+}
+
+// Default implementation for creating a new BertTokenizer.
+// Downloads tokenizer from given model_name (eg: "roberta-base").
+// Pad_token_id is the id of the padding token used to convert sequences to a consistent length.
+// specified in the model's config.json.
+impl BertTokenizer {
+    pub fn new(model_name: String, pad_token_id: usize) -> Self {
+        Self {
+            tokenizer: tokenizers::Tokenizer::from_pretrained(model_name, None).unwrap(),
+            pad_token: pad_token_id,
+        }
+    }
+}
+
+// Implementation of the Tokenizer trait for BertTokenizer.
+impl Tokenizer for BertTokenizer {
+    /// Convert a text string into a sequence of tokens using the BERT model's tokenization strategy.
+    fn encode(&self, value: &str) -> Vec<usize> {
+        let tokens = self.tokenizer.encode(value, true).unwrap();
+        tokens.get_ids().iter().map(|t| *t as usize).collect()
+    }
+
+    /// Converts a sequence of tokens back into a text string.
+    fn decode(&self, tokens: &[usize]) -> String {
+        let tokens = tokens.iter().map(|t| *t as u32).collect::<Vec<u32>>();
+        self.tokenizer.decode(&tokens, false).unwrap()
+    }
+
+    /// Gets the size of the BERT tokenizer's vocabulary.
+    fn vocab_size(&self) -> usize {
+        self.tokenizer.get_vocab_size(true)
+    }
+
+    /// Gets the token used for padding sequences to a consistent length.
+    fn pad_token(&self) -> usize {
+        self.pad_token
+    }
+}