use candle_core::DType;
use candle_core::Tensor;
use candle_nn::VarBuilder;
use super::config::XlmRobertaConfig;
use super::embeddings::XlmRobertaEmbeddings;
use crate::error::Result;
use crate::layers::mask::AttentionMask;
use crate::models::Encoder;
use crate::models::EncoderInput;
use crate::models::bert::layer::BertLayer;
pub struct XlmRobertaModel {
embeddings: XlmRobertaEmbeddings,
layers: Vec<BertLayer>,
config: XlmRobertaConfig,
}
impl std::fmt::Debug for XlmRobertaModel {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("XlmRobertaModel").finish_non_exhaustive()
}
}
impl XlmRobertaModel {
pub fn load(config: XlmRobertaConfig, vb: VarBuilder) -> Result<Self> {
config.ensure_supported()?;
let embeddings = XlmRobertaEmbeddings::load(&config, vb.pp("embeddings"))?;
let layers = (0..config.bert.num_hidden_layers)
.map(|i| BertLayer::load(&config.bert, vb.pp(format!("encoder.layer.{i}"))))
.collect::<Result<Vec<_>>>()?;
Ok(Self {
embeddings,
layers,
config,
})
}
}
impl Encoder for XlmRobertaModel {
fn forward(&self, input: &EncoderInput) -> Result<Tensor> {
let mut hidden = self
.embeddings
.forward(input.input_ids, input.token_type_ids)?;
let mask = AttentionMask::padding(
input.attention_mask,
input.lengths,
self.config.bert.num_attention_heads,
self.config.head_dim(),
hidden.dtype(),
)?;
for layer in &self.layers {
hidden = layer.forward(&hidden, &mask)?;
}
Ok(hidden)
}
fn hidden_size(&self) -> usize {
self.config.bert.hidden_size
}
fn max_position_embeddings(&self) -> usize {
self.config.sequence_limit()
}
fn weights_dtype(&self) -> DType {
self.embeddings.weights_dtype()
}
}
#[cfg(test)]
mod tests;