Add LM head and masked fill_mask for bert-burn.

bert-burn/examples/masked.rs

@@ -0,0 +1,150 @@
+use bert_burn::data::{BertInputBatcher, BertTokenizer};
+use bert_burn::fill_mask::fill_mask;
+use bert_burn::loader::{download_hf_model, load_model_config};
+use bert_burn::model::{BertMaskedLM, BertMaskedLMRecord};
+use burn::data::dataloader::batcher::Batcher;
+use burn::module::Module;
+use burn::tensor::backend::Backend;
+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![
+        "Paris is the <mask> of France.".to_string(),
+        "The goal of life is <mask>.".to_string(),
+    ];
+
+    let (config_file, model_file) = download_hf_model(model_variant);
+    let model_config = load_model_config(config_file);
+
+    let model_record: BertMaskedLMRecord<B> =
+        BertMaskedLM::from_safetensors(model_file, &device, model_config.clone());
+
+    let model = model_config
+        .init_with_lm_head(&device)
+        .load_record(model_record);
+
+    let tokenizer = Arc::new(BertTokenizer::new(
+        model_variant.to_string(),
+        model_config.pad_token_id,
+    ));
+
+    // 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(),
+    ));
+
+    // 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 = fill_mask(&model, &model_config, tokenizer.as_ref(), input);
+
+    for i in 0..batch_size {
+        let input = &text_samples[i];
+        let result = &output[i];
+        println!("Input: {}", input);
+        for fill_mask_result in result.iter() {
+            let mask_idx = fill_mask_result.mask_idx;
+            let top_k = &fill_mask_result.top_k;
+            for (k, (score, token)) in top_k.iter().enumerate() {
+                println!(
+                    "Top {} Prediction for {}: {} (Score: {:.4})",
+                    k + 1,
+                    mask_idx,
+                    token,
+                    score
+                );
+            }
+        }
+    }
+}
+
+#[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};
+
+    pub fn run() {
+        launch::<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/fill_mask.rs

@@ -0,0 +1,94 @@
+use crate::{
+    data::Tokenizer,
+    data::{BertInferenceBatch, BertTokenizer},
+    model::BertMaskedLM,
+    model::BertModelConfig,
+};
+use burn::tensor::{activation::softmax, backend::Backend, Data, Element, Tensor};
+
+type TokenType = usize;
+const MASK_TOKEN_ID: TokenType = 50264;
+
+#[derive(Debug, Clone)]
+pub struct FillMaskResult {
+    pub mask_idx: usize,
+    pub top_k: Vec<(f32, String)>,
+}
+
+pub fn fill_mask<B: Backend>(
+    model: &BertMaskedLM<B>,
+    model_config: &BertModelConfig,
+    tokenizer: &BertTokenizer,
+    input: BertInferenceBatch<B>,
+) -> Vec<Vec<FillMaskResult>> {
+    let [batch_size, seq_len] = input.tokens.dims();
+    let output = model.forward(input.clone());
+
+    let mut results = vec![];
+
+    // Embedding size
+    let d_model = model_config.vocab_size.clone();
+    for i in 0..batch_size {
+        let mut batch_results = vec![];
+        let input_tokens = input
+            .tokens
+            .clone()
+            .slice([i..i + 1, 0..seq_len])
+            .squeeze(0)
+            .to_data();
+        // Find the mask tokens in the input, as a list of indices
+        let masks = find_masks(&input_tokens, MASK_TOKEN_ID);
+        for mask in masks {
+            let logits = output
+                .clone()
+                .slice([i..i + 1, mask..(mask + 1), 0..d_model])
+                .squeeze::<2>(0)
+                .squeeze(0);
+            // Find the top k tokens with the highest probabilities
+            let top_k = top_k(5, logits);
+            batch_results.push(FillMaskResult {
+                mask_idx: mask,
+                top_k: top_k
+                    .iter()
+                    .map(|(k, prob)| (*prob, tokenizer.decode(&[*k])))
+                    .collect(),
+            });
+        }
+        results.push(batch_results);
+    }
+
+    results
+}
+
+fn find_masks<T: Element>(tokens: &Data<T, 1>, mask_token_id: TokenType) -> Vec<usize> {
+    let mut masks = Vec::new();
+    for (i, token) in tokens.value.iter().enumerate() {
+        if token.to_usize() == Some(mask_token_id) {
+            masks.push(i);
+        }
+    }
+    masks
+}
+
+fn data_to_vec_f32<T: Element>(data: &Data<T, 1>) -> Vec<f32> {
+    data.value.iter().map(|x| x.to_f32().unwrap()).collect()
+}
+
+fn data_to_vec_usize<T: Element>(data: &Data<T, 1>) -> Vec<usize> {
+    data.value.iter().map(|x| x.to_usize().unwrap()).collect()
+}
+
+fn top_k<B: Backend>(k: usize, logits: Tensor<B, 1>) -> Vec<(usize, f32)> {
+    let (pre_soft_probs, indices) = logits.sort_with_indices(0);
+    let (probabilities, indices) = (
+        data_to_vec_f32(&softmax(pre_soft_probs, 0).to_data()),
+        data_to_vec_usize(&indices.to_data()),
+    );
+    probabilities
+        .iter()
+        .enumerate()
+        .rev()
+        .take(k)
+        .map(|(i, &p)| (indices[i], p))
+        .collect()
+}

bert-burn/src/lib.rs

@@ -3,6 +3,7 @@ extern crate derive_new;
 
 pub mod data;
 mod embedding;
+pub mod fill_mask;
 pub mod loader;
 pub mod model;
 pub mod pooler;

bert-burn/src/loader.rs

@@ -17,7 +17,7 @@ use candle_core::Tensor as CandleTensor;
 use std::collections::HashMap;
 use std::path::PathBuf;
 
-fn load_1d_tensor_from_candle<B: Backend>(
+pub(crate) fn load_1d_tensor_from_candle<B: Backend>(
     tensor: &CandleTensor,
     device: &B::Device,
 ) -> Tensor<B, 1> {
@@ -29,7 +29,7 @@ fn load_1d_tensor_from_candle<B: Backend>(
     weight
 }
 
-fn load_2d_tensor_from_candle<B: Backend>(
+pub(crate) fn load_2d_tensor_from_candle<B: Backend>(
     tensor: &CandleTensor,
     device: &B::Device,
 ) -> Tensor<B, 2> {
@@ -46,7 +46,7 @@ fn load_2d_tensor_from_candle<B: Backend>(
     weight
 }
 
-fn load_layer_norm_safetensor<B: Backend>(
+pub(crate) fn load_layer_norm_safetensor<B: Backend>(
     bias: &CandleTensor,
     weight: &CandleTensor,
     device: &B::Device,
@@ -62,7 +62,7 @@ fn load_layer_norm_safetensor<B: Backend>(
     layer_norm_record
 }
 
-fn load_linear_safetensor<B: Backend>(
+pub(crate) fn load_linear_safetensor<B: Backend>(
     bias: &CandleTensor,
     weight: &CandleTensor,
     device: &B::Device,
@@ -79,7 +79,7 @@ fn load_linear_safetensor<B: Backend>(
     linear_record
 }
 
-fn load_intermediate_layer_safetensor<B: Backend>(
+pub(crate) fn load_intermediate_layer_safetensor<B: Backend>(
     linear_inner_weight: &CandleTensor,
     linear_inner_bias: &CandleTensor,
     linear_outer_weight: &CandleTensor,
@@ -227,6 +227,22 @@ pub fn load_encoder_from_safetensors<B: Backend>(
     encoder_record
 }
 
+pub fn load_decoder_from_safetensors<B: Backend>(
+    bias: &CandleTensor,
+    word_embedding_weights: &CandleTensor,
+    device: &B::Device,
+) -> LinearRecord<B> {
+    let bias = load_1d_tensor_from_candle::<B>(bias, device);
+    let weight = load_2d_tensor_from_candle::<B>(word_embedding_weights, device);
+    let weight = weight.transpose();
+
+    let linear_record = LinearRecord {
+        weight: Param::from_tensor(weight),
+        bias: Some(Param::from_tensor(bias)),
+    };
+    linear_record
+}
+
 fn load_embedding_safetensor<B: Backend>(
     weight: &CandleTensor,
     device: &B::Device,