rust-bert 0.7.9

// Copyright 2018 Google AI and Google Brain team.
// Copyright 2020-present, the HuggingFace Inc. team.
// Copyright 2020 Guillaume Becquin
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//     http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

use crate::albert::AlbertConfig;
use crate::common::dropout::Dropout;
use tch::nn::{embedding, EmbeddingConfig};
use tch::{nn, Kind, Tensor};

/// # Embeddings implementation for Albert model
#[derive(Debug)]
/// # Embeddings implementation for Electra model
pub struct AlbertEmbeddings {
    word_embeddings: nn::Embedding,
    position_embeddings: nn::Embedding,
    token_type_embeddings: nn::Embedding,
    layer_norm: nn::LayerNorm,
    dropout: Dropout,
}

impl AlbertEmbeddings {
    pub fn new(p: &nn::Path, config: &AlbertConfig) -> AlbertEmbeddings {
        let embedding_config = EmbeddingConfig {
            padding_idx: config.pad_token_id,
            ..Default::default()
        };

        let word_embeddings: nn::Embedding = embedding(
            p / "word_embeddings",
            config.vocab_size,
            config.embedding_size,
            embedding_config,
        );

        let position_embeddings: nn::Embedding = embedding(
            p / "position_embeddings",
            config.max_position_embeddings,
            config.embedding_size,
            Default::default(),
        );

        let token_type_embeddings: nn::Embedding = embedding(
            p / "token_type_embeddings",
            config.type_vocab_size,
            config.embedding_size,
            Default::default(),
        );

        let layer_norm_eps = match config.layer_norm_eps {
            Some(value) => value,
            None => 1e-12,
        };
        let layer_norm_config = nn::LayerNormConfig {
            eps: layer_norm_eps,
            ..Default::default()
        };
        let layer_norm: nn::LayerNorm = nn::layer_norm(
            p / "LayerNorm",
            vec![config.embedding_size],
            layer_norm_config,
        );
        let dropout: Dropout = Dropout::new(config.hidden_dropout_prob);
        AlbertEmbeddings {
            word_embeddings,
            position_embeddings,
            token_type_embeddings,
            layer_norm,
            dropout,
        }
    }

    pub 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) => {
                    let size = vec![embeds.size()[0], embeds.size()[1]];
                    (embeds, size)
                }
                None => {
                    return Err("Only one of input ids or input embeddings may be set");
                }
            },
        };

        let seq_length = input_embeddings.as_ref().size()[1].to_owned();

        let position_ids = match position_ids {
            Some(value) => value,
            None => Tensor::arange(seq_length, (Kind::Int64, input_embeddings.device()))
                .unsqueeze(0)
                .expand(&input_shape, true),
        };

        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))
    }
}