fn forward_t(
        &self,
        input_ids: Option<Tensor>,
        token_type_ids: Option<Tensor>,
        position_ids: Option<Tensor>,
        input_embeds: Option<Tensor>,
        train: bool,
    ) -> Result<Tensor, &'static str> {
        let (input_embeddings, input_shape) = match &input_ids {
            Some(input_value) => match &input_embeds {
                Some(_) => {
                    return Err("Only one of input ids or input embeddings may be set");
                }
                None => (
                    input_value.apply_t(&self.word_embeddings, train),
                    input_value.size(),
                ),
            },
            None => match &input_embeds {
                Some(embeds) => (embeds.copy(), vec![embeds.size()[0], embeds.size()[1]]),
                None => {
                    return Err("Only one of input ids or input embeddings may be set");
                }
            },
        };

        let position_ids = match position_ids {
            Some(value) => value,
            None => match input_ids {
                Some(value) => self.create_position_ids_from_input_ids(&value),
                None => self.create_position_ids_from_embeddings(&input_embeds.unwrap()),
            },
        };

        let token_type_ids = match token_type_ids {
            Some(value) => value,
            None => Tensor::zeros(&input_shape, (Kind::Int64, input_embeddings.device())),
        };

        let position_embeddings = position_ids.apply(&self.position_embeddings);
        let token_type_embeddings = token_type_ids.apply(&self.token_type_embeddings);

        let input_embeddings: Tensor =
            input_embeddings + position_embeddings + token_type_embeddings;
        Ok(input_embeddings
            .apply(&self.layer_norm)
            .apply_t(&self.dropout, train))
    }
/// Forward pass through the embedding layer.
/// This differs from the original BERT embeddings in how the position ids are calculated when not provided.
///
/// # Arguments
///
/// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see *input_embeds*)
/// * `token_type_ids` -Optional segment id of shape (*batch size*, *sequence_length*). Convention is value of 0 for the first sentence (incl. *[SEP]*) and 1 for the second sentence. If None set to 0.
/// * `position_ids` - Optional position ids of shape (*batch size*, *sequence_length*). If None, will be incremented from 0.
/// * `input_embeds` - Optional pre-computed input embeddings of shape (*batch size*, *sequence_length*, *hidden_size*). If None, input ids must be provided (see *input_ids*)
/// * `train` - boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.
///
/// # Returns
///
/// * `embedded_output` - `Tensor` of shape (*batch size*, *sequence_length*, *hidden_size*)
///
/// # Example
///
/// ```no_run
/// # use rust_bert::bert::{BertConfig, BertEmbedding};
/// # use tch::{nn, Device, Tensor, no_grad};
/// # use rust_bert::Config;
/// # use std::path::Path;
/// # use tch::kind::Kind::Int64;
/// use rust_bert::roberta::RobertaEmbeddings;
/// # let config_path = Path::new("path/to/config.json");
/// # let vocab_path = Path::new("path/to/vocab.txt");
/// # let device = Device::Cpu;
/// # let vs = nn::VarStore::new(device);
/// # let config = BertConfig::from_file(config_path);
/// # let roberta_embeddings = RobertaEmbeddings::new(&vs.root(), &config);
/// let (batch_size, sequence_length) = (64, 128);
/// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device));
/// let token_type_ids = Tensor::zeros(&[batch_size, sequence_length], (Int64, device));
/// let position_ids = Tensor::arange(sequence_length, (Int64, device))
///     .expand(&[batch_size, sequence_length], true);
///
/// let embedded_output = no_grad(|| {
///     roberta_embeddings
///         .forward_t(
///             Some(input_tensor),
///             Some(token_type_ids),
///             Some(position_ids),
///             None,
///             false,
///         )
///         .unwrap()
/// });
/// ```
Forward pass through the embedding layer.
This differs from the original BERT embeddings in how the position ids are calculated when not provided.
Forward pass through the embedding layer . This differs from the original BERT embeddings in how the position ids are calculated when not provided .
fn forward_t(
        &self,
        input_ids: Option<Tensor>,
        token_type_ids: Option<Tensor>,
        position_ids: Option<Tensor>,
        input_embeds: Option<Tensor>,
        train: bool,
    ) -> Result<Tensor, &'static str> {
        let (input_embeddings, input_shape) = match &input_ids {
            Some(input_value) => match &input_embeds {
                Some(_) => {
                    return Err("Only one of input ids or input embeddings may be set");
                }
                None => (
                    input_value.apply_t(&self.word_embeddings, train),
                    input_value.size(),
                ),
            },
            None => match &input_embeds {
                Some(embeds) => (embeds.copy(), vec![embeds.size()[0], embeds.size()[1]]),
                None => {
                    return Err("Only one of input ids or input embeddings may be set");
                }
            },
        };
        let position_ids = match position_ids {
            Some(value) => value,
            None => match input_ids {
                Some(value) => self.create_position_ids_from_input_ids(&value),
                None => self.create_position_ids_from_embeddings(&input_embeds.unwrap()),
            },
        };
        let token_type_ids = match token_type_ids {
            Some(value) => value,
            None => Tensor::zeros(&input_shape, (Kind::Int64, input_embeddings.device())),
        };
        let position_embeddings = position_ids.apply(&self.position_embeddings);
        let token_type_embeddings = token_type_ids.apply(&self.token_type_embeddings);
        let input_embeddings: Tensor =
            input_embeddings + position_embeddings + token_type_embeddings;
        Ok(input_embeddings
            .apply(&self.layer_norm)
            .apply_t(&self.dropout, train))
    }
fn forward_t ( & self , input_ids : Option < Tensor > , token_type_ids : Option < Tensor > , position_ids : Option < Tensor > , input_embeds : Option < Tensor > , train : bool , ) -> Result < Tensor , & ' static str > { let ( input_embeddings , input_shape ) = match & input_ids { Some ( input_value ) => match & input_embeds { Some ( _ ) => { return Err ( " " ) ; } None => ( input_value . apply_t ( & self . word_embeddings , train ) , input_value . size ( ) , ) , } , None => match & input_embeds { Some ( embeds ) => ( embeds . copy ( ) , vec ! [ embeds size ( ) [ 0 ] embeds size ( ) [ 1 ] ] ) , None => { return Err ( " " ) ; } } , } ; let position_ids = match position_ids { Some ( value ) => value , None => match input_ids { Some ( value ) => self . create_position_ids_from_input_ids ( & value ) , None => self . create_position_ids_from_embeddings ( & input_embeds . unwrap ( ) ) , } , } ; let token_type_ids = match token_type_ids { Some ( value ) => value , None => Tensor :: zeros ( & input_shape , ( Kind :: Int64 , input_embeddings . device ( ) ) ) , } ; let position_embeddings = position_ids . apply ( & self . position_embeddings ) ; let token_type_embeddings = token_type_ids . apply ( & self . token_type_embeddings ) ; let input_embeddings : Tensor = input_embeddings + position_embeddings + token_type_embeddings ; Ok ( input_embeddings . apply ( & self . layer_norm ) . apply_t ( & self . dropout , train ) ) }
Forward pass through the embedding layer.
Forward pass through the embedding layer .
&self
input_ids
Option<Tensor>
token_type_ids
Option<Tensor>
position_ids
Option<Tensor>
input_embeds
Option<Tensor>
train
bool
&self
input_ids
Option<Tensor>
token_type_ids
Option<Tensor>
position_ids
Option<Tensor>
input_embeds
Option<Tensor>
train
bool
example
None
use rust_bert::bert::{bertconfig, bertembedding};
None
use tch::{nn, device, tensor, no_grad};
None
use rust_bert::config;
None
use std::path::path;
None
use tch::kind::kind::int64;
None
let config_path = path::new("path/to/config.json");
None
let vocab_path = path::new("path/to/vocab.txt");
None
let device = device::cpu;
None
let vs = nn::varstore::new(device);
None
let config = bertconfig::from_file(config_path);
None
let roberta_embeddings = robertaembeddings::new(&vs.root(), &config);
let (batch_size, sequence_length) = (64, 128);
let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device));
let token_type_ids = Tensor::zeros(&[batch_size, sequence_length], (Int64, device));
let position_ids = Tensor::arange(sequence_length, (Int64, device))
.expand(&[batch_size, sequence_length], true).


let ( batch_size sequence_length ) = ( 64 128 ) ; let input_tensor = Tensor :: rand ( & [ batch_size sequence_length ] ( Int64 device )) ; let token_type_ids = Tensor :: zeros ( & [ batch_size sequence_length ] ( Int64 device )) ; let position_ids = Tensor :: arange ( sequence_length ( Int64 device )) . expand ( & [ batch_size sequence_length ] true ) .