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Added XLMRobertaModel for Reranking (#2686)
* add xlm-roberta-base * Add task enum for fill-mask and reranker in xlm-roberta example; update README and fix attention mask dimensions - Introduced a new `Task` enum to replace string task identifiers in the xlm-roberta example. - Updated the logic in `main.rs` to handle tasks using the new enum. - Enhanced README with example output for fill-mask task. - Fixed dimension retrieval in `prepare_4d_attention_mask` function for better clarity and safety. * Clippy fix. --------- Co-authored-by: laurent <laurent.mazare@gmail.com>
This commit is contained in:
Akshay Ballal
@ -109,4 +109,5 @@ pub mod vit;
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pub mod whisper;
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pub mod with_tracing;
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pub mod wuerstchen;
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pub mod xlm_roberta;
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pub mod yi;
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545
candle-transformers/src/models/xlm_roberta.rs
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545
candle-transformers/src/models/xlm_roberta.rs
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@ -0,0 +1,545 @@
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use crate::models::with_tracing::{linear, Linear};
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use candle::{DType, Module, Result, Tensor};
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use candle_nn::{
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embedding, layer_norm, ops::softmax_last_dim, Activation, Embedding, LayerNorm, VarBuilder,
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};
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#[derive(Debug, Clone, serde::Deserialize)]
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pub struct Config {
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pub hidden_size: usize,
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pub layer_norm_eps: f64,
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pub attention_probs_dropout_prob: f32,
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pub hidden_dropout_prob: f32,
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pub num_attention_heads: usize,
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pub position_embedding_type: String,
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pub intermediate_size: usize,
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pub hidden_act: Activation,
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pub num_hidden_layers: usize,
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pub vocab_size: usize,
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pub max_position_embeddings: usize,
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pub type_vocab_size: usize,
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pub pad_token_id: u32,
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}
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struct XLMRobertaEmbeddings {
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word_embeddings: Embedding,
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position_embeddings: Option<Embedding>,
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token_type_embeddings: Embedding,
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layer_norm: LayerNorm,
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padding_idx: u32,
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span: tracing::Span,
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}
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impl XLMRobertaEmbeddings {
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fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
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let word_embeddings = embedding(
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config.vocab_size,
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config.hidden_size,
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vb.pp("word_embeddings"),
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)?;
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let position_embeddings = embedding(
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config.max_position_embeddings,
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config.hidden_size,
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vb.pp("position_embeddings"),
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)?;
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let token_type_embeddings = embedding(
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config.type_vocab_size,
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config.hidden_size,
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vb.pp("token_type_embeddings"),
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)?;
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let layer_norm = layer_norm(
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config.hidden_size,
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config.layer_norm_eps,
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vb.pp("LayerNorm"),
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)?;
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Ok(Self {
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word_embeddings,
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position_embeddings: Some(position_embeddings),
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token_type_embeddings,
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layer_norm,
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padding_idx: config.pad_token_id,
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span: tracing::span!(tracing::Level::TRACE, "embeddings"),
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})
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}
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fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
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let _enter = self.span.enter();
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let (_bsize, _) = input_ids.dims2()?;
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let input_embeddings = self.word_embeddings.forward(input_ids)?;
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let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
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let mut embeddings = (&input_embeddings + token_type_embeddings)?;
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if let Some(position_embeddings) = &self.position_embeddings {
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let mask = input_ids
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.ne(self.padding_idx)?
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.to_dtype(input_embeddings.dtype())?;
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let cumsum = mask.cumsum(1)?;
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let position_ids = (cumsum * mask)?
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.broadcast_add(
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&Tensor::try_from(self.padding_idx)?
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.to_dtype(input_embeddings.dtype())?
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.to_device(input_embeddings.device())?,
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)?
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.to_dtype(candle::DType::U32)?;
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embeddings = embeddings.broadcast_add(&position_embeddings.forward(&position_ids)?)?;
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}
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let embeddings = self.layer_norm.forward(&embeddings)?;
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Ok(embeddings)
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}
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}
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struct XLMRobertaSelfAttention {
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num_attention_heads: usize,
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attention_head_size: usize,
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all_head_size: usize,
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query: Linear,
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key: Linear,
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value: Linear,
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}
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impl XLMRobertaSelfAttention {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let attention_head_size = cfg.hidden_size / cfg.num_attention_heads;
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let all_head_size = cfg.num_attention_heads * attention_head_size;
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Ok(Self {
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num_attention_heads: cfg.num_attention_heads,
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attention_head_size,
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all_head_size,
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query: linear(cfg.hidden_size, all_head_size, vb.pp("query"))?,
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key: linear(cfg.hidden_size, all_head_size, vb.pp("key"))?,
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value: linear(cfg.hidden_size, all_head_size, vb.pp("value"))?,
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})
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}
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fn transpose_for_scores(&self, x: &Tensor) -> Result<Tensor> {
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let mut new_x_shape = x.dims().to_vec();
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new_x_shape[2] = self.num_attention_heads;
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new_x_shape.push(self.attention_head_size);
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let x = x.reshape(new_x_shape)?;
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x.permute((0, 2, 1, 3))?.contiguous()
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}
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fn forward(
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&self,
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hidden_states: &Tensor,
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encoder_hidden_states: Option<&Tensor>,
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attention_mask: &Tensor,
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past_key_value: Option<(&Tensor, &Tensor)>,
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encoder_attention_mask: Option<&Tensor>,
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) -> Result<Tensor> {
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let mixed_query_layer = self.query.forward(hidden_states)?;
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let is_cross_attention = encoder_hidden_states.is_some();
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let (key_layer, value_layer, attention_mask) = if is_cross_attention
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&& past_key_value.is_some()
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{
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let key_layer = past_key_value.unwrap().0.clone();
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let value_layer = past_key_value.unwrap().1.clone();
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let attention_mask = encoder_attention_mask.unwrap().clone();
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(key_layer, value_layer, Some(attention_mask))
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} else if is_cross_attention {
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let key_layer =
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self.transpose_for_scores(&self.key.forward(encoder_hidden_states.unwrap())?)?;
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let value_layer =
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self.transpose_for_scores(&self.value.forward(encoder_hidden_states.unwrap())?)?;
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let attention_mask = encoder_attention_mask.unwrap();
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(key_layer, value_layer, Some(attention_mask.clone()))
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} else if past_key_value.is_some() {
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let mut key_layer = self.transpose_for_scores(&self.key.forward(hidden_states)?)?;
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let mut value_layer = self.transpose_for_scores(&self.value.forward(hidden_states)?)?;
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key_layer = Tensor::cat(
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&[
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past_key_value.clone().as_ref().unwrap().0.clone(),
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key_layer,
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],
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2,
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)?;
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value_layer = Tensor::cat(
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&[past_key_value.as_ref().unwrap().1.clone(), value_layer],
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2,
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)?;
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(key_layer, value_layer, Some(attention_mask.clone()))
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} else {
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let key_layer = self.transpose_for_scores(&self.key.forward(hidden_states)?)?;
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let value_layer = self.transpose_for_scores(&self.value.forward(hidden_states)?)?;
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(key_layer, value_layer, Some(attention_mask.clone()))
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};
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let query_layer = self.transpose_for_scores(&mixed_query_layer)?;
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let mut attention_scores = query_layer.matmul(&key_layer.transpose(2, 3)?)?;
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let scale = 1f64 / f64::sqrt(self.attention_head_size as f64);
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attention_scores = (attention_scores * scale)?;
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attention_scores = match attention_mask {
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None => attention_scores,
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Some(mask) => {
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attention_scores.broadcast_add(&mask.to_dtype(attention_scores.dtype())?)?
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}
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};
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let attention_probs = softmax_last_dim(&attention_scores)?;
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let context_layer = attention_probs
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.matmul(&value_layer)?
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.permute((0, 2, 1, 3))?
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.contiguous()?;
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let mut new_context_layer_shape =
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context_layer.dims()[..context_layer.dims().len() - 2].to_vec();
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new_context_layer_shape.push(self.all_head_size);
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let context_layer = context_layer.reshape(new_context_layer_shape)?;
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Ok(context_layer)
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}
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}
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struct XLMRobertaSelfOutput {
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dense: Linear,
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layernorm: LayerNorm,
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}
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impl XLMRobertaSelfOutput {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let dense = linear(cfg.hidden_size, cfg.hidden_size, vb.pp("dense"))?;
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let layernorm =
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candle_nn::layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb.pp("LayerNorm"))?;
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Ok(Self { dense, layernorm })
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}
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fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
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let hidden_states = self.dense.forward(hidden_states)?;
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let hidden_states = self.layernorm.forward(&(hidden_states + input_tensor)?)?;
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Ok(hidden_states)
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}
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}
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struct XLMRobertaAttention {
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output: XLMRobertaSelfOutput,
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self_attention: XLMRobertaSelfAttention,
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}
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impl XLMRobertaAttention {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let output = XLMRobertaSelfOutput::new(cfg, vb.pp("output"))?;
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let self_attention = XLMRobertaSelfAttention::new(cfg, vb.pp("self"))?;
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Ok(Self {
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output,
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self_attention,
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})
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}
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fn forward(
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&self,
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hidden_states: &Tensor,
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attention_mask: &Tensor,
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encoder_hidden_states: Option<&Tensor>,
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encoder_attention_mask: Option<&Tensor>,
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past_key_value: Option<(&Tensor, &Tensor)>,
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) -> Result<(Tensor, Tensor)> {
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let self_outputs = self.self_attention.forward(
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hidden_states,
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encoder_hidden_states,
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attention_mask,
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past_key_value,
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encoder_attention_mask,
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)?;
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let attention_output = self.output.forward(&self_outputs, hidden_states)?;
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Ok((attention_output, self_outputs))
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}
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}
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struct XLMRobertaOutput {
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dense: Linear,
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layernorm: LayerNorm,
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}
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impl XLMRobertaOutput {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let dense = linear(cfg.intermediate_size, cfg.hidden_size, vb.pp("dense"))?;
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let layernorm =
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candle_nn::layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb.pp("LayerNorm"))?;
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Ok(Self { dense, layernorm })
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}
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fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
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let hidden_states = self.dense.forward(hidden_states)?;
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let hidden_states = self.layernorm.forward(&(hidden_states + input_tensor)?)?;
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Ok(hidden_states)
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}
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}
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struct XLMRobertaIntermediate {
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dense: Linear,
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intermediate_act_fn: Activation,
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}
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impl XLMRobertaIntermediate {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let dense = linear(cfg.hidden_size, cfg.intermediate_size, vb.pp("dense"))?;
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let intermediate_act_fn = cfg.hidden_act;
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Ok(Self {
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dense,
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intermediate_act_fn,
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})
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}
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fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
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let hidden_states = self.dense.forward(hidden_states)?;
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let hidden_states = self.intermediate_act_fn.forward(&hidden_states)?;
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Ok(hidden_states)
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}
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}
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struct XLMRobertaLayer {
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attention: XLMRobertaAttention,
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intermediate: XLMRobertaIntermediate,
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output: XLMRobertaOutput,
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}
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impl XLMRobertaLayer {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let attention = XLMRobertaAttention::new(cfg, vb.pp("attention"))?;
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let intermediate = XLMRobertaIntermediate::new(cfg, vb.pp("intermediate"))?;
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let output = XLMRobertaOutput::new(cfg, vb.pp("output"))?;
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Ok(Self {
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attention,
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intermediate,
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output,
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})
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}
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fn forward(
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&self,
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hidden_states: &Tensor,
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attention_mask: &Tensor,
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encoder_hidden_states: Option<&Tensor>,
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encoder_attention_mask: Option<&Tensor>,
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past_key_value: Option<(&Tensor, &Tensor)>,
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) -> Result<(Tensor, Tensor)> {
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let self_attention_outputs = self.attention.forward(
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hidden_states,
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attention_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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)?;
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let attention_output = self_attention_outputs.0;
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let outputs = self_attention_outputs.1;
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let intermediate_output = self.intermediate.forward(&attention_output)?;
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let layer_output = self
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.output
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.forward(&intermediate_output, &attention_output)?;
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Ok((layer_output, outputs))
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}
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}
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struct XLMRobertaEncoder {
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layers: Vec<XLMRobertaLayer>,
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}
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impl XLMRobertaEncoder {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let layers = (0..cfg.num_hidden_layers)
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.map(|i| XLMRobertaLayer::new(cfg, vb.pp(format!("layer.{}", i))))
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.collect::<Result<Vec<_>>>()?;
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Ok(Self { layers })
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}
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fn forward(
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&self,
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hidden_states: &Tensor,
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attention_mask: &Tensor,
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encoder_hidden_states: Option<&Tensor>,
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encoder_attention_mask: Option<&Tensor>,
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past_key_value: Option<(&Tensor, &Tensor)>,
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) -> Result<Tensor> {
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let mut hidden_states = hidden_states.clone();
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for layer_module in self.layers.iter() {
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let layer_outputs = layer_module.forward(
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&hidden_states,
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attention_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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)?;
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hidden_states = layer_outputs.0;
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}
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Ok(hidden_states)
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}
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}
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pub struct XLMRobertaModel {
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encoder: XLMRobertaEncoder,
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embeddings: XLMRobertaEmbeddings,
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}
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impl XLMRobertaModel {
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pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let encoder = XLMRobertaEncoder::new(cfg, vb.pp("encoder"))?;
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let embeddings = XLMRobertaEmbeddings::load(vb.pp("embeddings"), cfg)?;
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Ok(Self {
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encoder,
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embeddings,
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})
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}
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pub fn forward(
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&self,
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input_ids: &Tensor,
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attention_mask: &Tensor,
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token_type_ids: &Tensor,
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past_key_value: Option<(&Tensor, &Tensor)>,
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encoder_hidden_states: Option<&Tensor>,
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encoder_attention_mask: Option<&Tensor>,
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) -> Result<Tensor> {
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let hidden_states = self.embeddings.forward(input_ids, token_type_ids)?;
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let attention_mask = prepare_4d_attention_mask(attention_mask, DType::F32, None)?
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.to_device(hidden_states.device())?;
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let hidden_states = self.encoder.forward(
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&hidden_states,
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&attention_mask,
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encoder_hidden_states,
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encoder_attention_mask,
|
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past_key_value,
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)?;
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Ok(hidden_states)
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}
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}
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|
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struct XLMRobertaLMHead {
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dense: Linear,
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layer_norm: LayerNorm,
|
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}
|
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|
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impl XLMRobertaLMHead {
|
||||
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
|
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let dense = linear(cfg.hidden_size, cfg.hidden_size, vb.pp("dense"))?;
|
||||
let layer_norm =
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candle_nn::layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb.pp("layer_norm"))?;
|
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Ok(Self { dense, layer_norm })
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}
|
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|
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fn forward(&self, hidden_states: &Tensor, shared_embeddings: &Tensor) -> Result<Tensor> {
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let hidden_states = self.dense.forward(hidden_states)?;
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let hidden_states = candle_nn::Activation::Gelu.forward(&hidden_states)?;
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let hidden_states = self.layer_norm.forward(&hidden_states)?;
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let hidden_states = hidden_states.broadcast_matmul(shared_embeddings)?;
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Ok(hidden_states)
|
||||
}
|
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}
|
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pub struct XLMRobertaForMaskedLM {
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roberta: XLMRobertaModel,
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lm_head: XLMRobertaLMHead,
|
||||
}
|
||||
|
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impl XLMRobertaForMaskedLM {
|
||||
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let roberta = XLMRobertaModel::new(cfg, vb.pp("roberta"))?;
|
||||
let lm_head = XLMRobertaLMHead::new(cfg, vb.pp("lm_head"))?;
|
||||
Ok(Self { roberta, lm_head })
|
||||
}
|
||||
|
||||
pub fn forward(
|
||||
&self,
|
||||
input_ids: &Tensor,
|
||||
attention_mask: &Tensor,
|
||||
token_type_ids: &Tensor,
|
||||
past_key_value: Option<(&Tensor, &Tensor)>,
|
||||
encoder_hidden_states: Option<&Tensor>,
|
||||
encoder_attention_mask: Option<&Tensor>,
|
||||
) -> Result<Tensor> {
|
||||
let hidden_states = self.roberta.forward(
|
||||
input_ids,
|
||||
attention_mask,
|
||||
token_type_ids,
|
||||
past_key_value,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)?;
|
||||
let lm_logits = self.lm_head.forward(
|
||||
&hidden_states,
|
||||
&self
|
||||
.roberta
|
||||
.embeddings
|
||||
.word_embeddings
|
||||
.embeddings()
|
||||
.t()?
|
||||
.unsqueeze(0)?,
|
||||
)?;
|
||||
Ok(lm_logits)
|
||||
}
|
||||
}
|
||||
|
||||
struct XLMRobertaClassificationHead {
|
||||
dense: Linear,
|
||||
out_proj: Linear,
|
||||
}
|
||||
|
||||
impl XLMRobertaClassificationHead {
|
||||
fn new(num_labels: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
|
||||
let dense = linear(cfg.hidden_size, cfg.hidden_size, vb.pp("dense"))?;
|
||||
let out_proj = linear(cfg.hidden_size, num_labels, vb.pp("out_proj"))?;
|
||||
Ok(Self { dense, out_proj })
|
||||
}
|
||||
|
||||
fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
|
||||
let cls_states = hidden_states.get_on_dim(1, 0)?.contiguous()?;
|
||||
let hidden_states = self.dense.forward(&cls_states)?;
|
||||
let hidden_states = candle_nn::Activation::GeluPytorchTanh.forward(&hidden_states)?;
|
||||
let hidden_states = self.out_proj.forward(&hidden_states)?;
|
||||
Ok(hidden_states)
|
||||
}
|
||||
}
|
||||
|
||||
pub struct XLMRobertaForSequenceClassification {
|
||||
roberta: XLMRobertaModel,
|
||||
classifier: XLMRobertaClassificationHead,
|
||||
}
|
||||
|
||||
impl XLMRobertaForSequenceClassification {
|
||||
pub fn new(num_labels: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
|
||||
let roberta = XLMRobertaModel::new(cfg, vb.pp("roberta"))?;
|
||||
let classifier = XLMRobertaClassificationHead::new(num_labels, cfg, vb.pp("classifier"))?;
|
||||
Ok(Self {
|
||||
roberta,
|
||||
classifier,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn forward(
|
||||
&self,
|
||||
input_ids: &Tensor,
|
||||
attention_mask: &Tensor,
|
||||
token_type_ids: &Tensor,
|
||||
) -> Result<Tensor> {
|
||||
let hidden_states =
|
||||
self.roberta
|
||||
.forward(input_ids, attention_mask, token_type_ids, None, None, None)?;
|
||||
self.classifier.forward(&hidden_states)
|
||||
}
|
||||
}
|
||||
|
||||
fn prepare_4d_attention_mask(
|
||||
mask: &Tensor,
|
||||
dtype: DType,
|
||||
tgt_len: Option<usize>,
|
||||
) -> Result<Tensor> {
|
||||
let bsz = mask.dim(0)?;
|
||||
let src_len = mask.dim(1)?;
|
||||
let tgt_len = tgt_len.unwrap_or(src_len);
|
||||
|
||||
let expanded_mask = mask
|
||||
.unsqueeze(1)?
|
||||
.unsqueeze(2)?
|
||||
.expand((bsz, 1, tgt_len, src_len))?
|
||||
.to_dtype(dtype)?;
|
||||
|
||||
let inverted_mask = (1.0 - expanded_mask)?;
|
||||
|
||||
(inverted_mask * get_dtype_min_val(dtype))?.to_dtype(dtype)
|
||||
}
|
||||
|
||||
fn get_dtype_min_val(dtype: DType) -> f64 {
|
||||
match dtype {
|
||||
DType::F32 => f32::MIN as f64,
|
||||
DType::F64 => f64::MIN,
|
||||
_ => panic!("Unsupported data type"),
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user