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// 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::embeddings::AlbertEmbeddings; use crate::albert::encoder::AlbertTransformer; use crate::common::activations::{_gelu, _gelu_new, _mish, _relu, _tanh}; use crate::common::dropout::Dropout; use crate::Config; use serde::{Deserialize, Serialize}; use std::{borrow::Borrow, collections::HashMap}; use tch::nn::Module; use tch::{nn, Kind, Tensor}; /// # ALBERT Pretrained model weight files pub struct AlbertModelResources; /// # ALBERT Pretrained model config files pub struct AlbertConfigResources; /// # ALBERT Pretrained model vocab files pub struct AlbertVocabResources; impl AlbertModelResources { /// Shared under Apache 2.0 license by the Google team at https://github.com/google-research/ALBERT. Modified with conversion to C-array format. pub const ALBERT_BASE_V2: (&'static str, &'static str) = ( "albert-base-v2/model.ot", "https://cdn.huggingface.co/albert-base-v2/rust_model.ot", ); } impl AlbertConfigResources { /// Shared under Apache 2.0 license by the Google team at https://github.com/google-research/ALBERT. Modified with conversion to C-array format. pub const ALBERT_BASE_V2: (&'static str, &'static str) = ( "albert-base-v2/config.json", "https://cdn.huggingface.co/albert-base-v2-config.json", ); } impl AlbertVocabResources { /// Shared under Apache 2.0 license by the Google team at https://github.com/google-research/ALBERT. Modified with conversion to C-array format. pub const ALBERT_BASE_V2: (&'static str, &'static str) = ( "albert-base-v2/spiece.model", "https://cdn.huggingface.co/albert-base-v2-spiece.model", ); } #[allow(non_camel_case_types)] #[derive(Clone, Debug, Serialize, Deserialize)] /// # Activation function used in the attention layer and masked language model head pub enum Activation { /// Gaussian Error Linear Unit ([Hendrycks et al., 2016,](https://arxiv.org/abs/1606.08415)) gelu_new, /// Gaussian Error Linear Unit ([Hendrycks et al., 2016,](https://arxiv.org/abs/1606.08415)) gelu, /// Rectified Linear Unit relu, /// Mish ([Misra, 2019](https://arxiv.org/abs/1908.08681)) mish, } #[derive(Debug, Serialize, Deserialize)] /// # ALBERT model configuration /// Defines the ALBERT model architecture (e.g. number of layers, hidden layer size, label mapping...) pub struct AlbertConfig { pub hidden_act: Activation, pub attention_probs_dropout_prob: f64, pub classifier_dropout_prob: Option<f64>, pub bos_token_id: i64, pub eos_token_id: i64, pub down_scale_factor: i64, pub embedding_size: i64, pub gap_size: i64, pub hidden_dropout_prob: f64, pub hidden_size: i64, pub initializer_range: f32, pub inner_group_num: i64, pub intermediate_size: i64, pub layer_norm_eps: Option<f64>, pub max_position_embeddings: i64, pub net_structure_type: i64, pub num_attention_heads: i64, pub num_hidden_groups: i64, pub num_hidden_layers: i64, pub num_memory_blocks: i64, pub pad_token_id: i64, pub type_vocab_size: i64, pub vocab_size: i64, pub output_attentions: Option<bool>, pub output_hidden_states: Option<bool>, pub is_decoder: Option<bool>, pub id2label: Option<HashMap<i64, String>>, pub label2id: Option<HashMap<String, i64>>, } impl Config<AlbertConfig> for AlbertConfig {} /// # ALBERT Base model /// Base architecture for ALBERT models. Task-specific models will be built from this common base model /// It is made of the following blocks: /// - `embeddings`: `token`, `position` and `segment_id` embeddings /// - `encoder`: Encoder (transformer) made of a vector of layers. Each layer is made of a self-attention layer, an intermediate (linear) and output (linear + layer norm) layers. Note that the weights are shared across layers, allowing for a reduction in the model memory footprint. /// - `pooler`: linear layer applied to the first element of the sequence (*[MASK]* token) /// - `pooler_activation`: Tanh activation function for the pooling layer pub struct AlbertModel { embeddings: AlbertEmbeddings, encoder: AlbertTransformer, pooler: nn::Linear, pooler_activation: Box<dyn Fn(&Tensor) -> Tensor>, } impl AlbertModel { /// Build a new `AlbertModel` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertModel}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertModel = AlbertModel::new(&p.root() / "albert", &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertModel where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let embeddings = AlbertEmbeddings::new(p / "embeddings", config); let encoder = AlbertTransformer::new(p / "encoder", config); let pooler = nn::linear( p / "pooler", config.hidden_size, config.hidden_size, Default::default(), ); let pooler_activation = Box::new(_tanh); AlbertModel { embeddings, encoder, pooler, pooler_activation, } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `output` - `Tensor` of shape (*batch size*, *sequence_length*, *hidden_size*) /// * `pooled_output` - `Tensor` of shape (*batch size*, *hidden_size*) /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertModel}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertModel = AlbertModel::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (output, pooled_output, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model /// .forward_t( /// Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false, /// ) /// .unwrap() /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> Result< ( Tensor, Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>, ), &'static str, > { let (input_shape, device) = 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.size(), input_value.device()), }, None => match &input_embeds { Some(embeds) => (vec![embeds.size()[0], embeds.size()[1]], embeds.device()), None => { return Err("At least one of input ids or input embeddings must be set"); } }, }; let mask = match mask { Some(value) => value, None => Tensor::ones(&input_shape, (Kind::Int64, device)), }; let extended_attention_mask = mask.unsqueeze(1).unsqueeze(2); let extended_attention_mask: Tensor = (extended_attention_mask.ones_like() - extended_attention_mask) * -10000.0; let embedding_output = match self.embeddings.forward_t( input_ids, token_type_ids, position_ids, input_embeds, train, ) { Ok(value) => value, Err(e) => { return Err(e); } }; let (hidden_state, all_hidden_states, all_attentions) = self.encoder .forward_t(&embedding_output, Some(extended_attention_mask), train); let pooled_output = self.pooler.forward(&hidden_state.select(1, 0)); let pooled_output = (self.pooler_activation)(&pooled_output); Ok(( hidden_state, pooled_output, all_hidden_states, all_attentions, )) } } pub struct AlbertMLMHead { layer_norm: nn::LayerNorm, dense: nn::Linear, decoder: nn::Linear, activation: Box<dyn Fn(&Tensor) -> Tensor>, } impl AlbertMLMHead { pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertMLMHead where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); 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::layer_norm( p / "LayerNorm", vec![config.embedding_size], layer_norm_config, ); let dense = nn::linear( p / "dense", config.hidden_size, config.embedding_size, Default::default(), ); let decoder = nn::linear( p / "decoder", config.embedding_size, config.vocab_size, Default::default(), ); let activation = Box::new(match &config.hidden_act { Activation::gelu_new => _gelu_new, Activation::gelu => _gelu, Activation::relu => _relu, Activation::mish => _mish, }); AlbertMLMHead { layer_norm, dense, decoder, activation, } } pub fn forward(&self, hidden_states: &Tensor) -> Tensor { let output: Tensor = (self.activation)(&hidden_states.apply(&self.dense)); output.apply(&self.layer_norm).apply(&self.decoder) } } /// # ALBERT for masked language model /// Base ALBERT model with a masked language model head to predict missing tokens, for example `"Looks like one [MASK] is missing" -> "person"` /// It is made of the following blocks: /// - `albert`: Base AlbertModel /// - `predictions`: ALBERT MLM prediction head pub struct AlbertForMaskedLM { albert: AlbertModel, predictions: AlbertMLMHead, } impl AlbertForMaskedLM { /// Build a new `AlbertForMaskedLM` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertForMaskedLM}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertForMaskedLM = AlbertForMaskedLM::new(&p.root(), &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertForMaskedLM where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let albert = AlbertModel::new(p / "albert", config); let predictions = AlbertMLMHead::new(p / "predictions", config); AlbertForMaskedLM { albert, predictions, } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `output` - `Tensor` of shape (*batch size*, *sequence_length*, *vocab_size*) /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertForMaskedLM}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertForMaskedLM = AlbertForMaskedLM::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (output, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model.forward_t( /// Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false, /// ) /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) { let (hidden_state, _, all_hidden_states, all_attentions) = self .albert .forward_t( input_ids, mask, token_type_ids, position_ids, input_embeds, train, ) .unwrap(); let prediction_scores = self.predictions.forward(&hidden_state); (prediction_scores, all_hidden_states, all_attentions) } } /// # ALBERT for sequence classification /// Base ALBERT model with a classifier head to perform sentence or document-level classification /// It is made of the following blocks: /// - `albert`: Base AlbertModel /// - `dropout`: Dropout layer /// - `classifier`: linear layer for classification pub struct AlbertForSequenceClassification { albert: AlbertModel, dropout: Dropout, classifier: nn::Linear, } impl AlbertForSequenceClassification { /// Build a new `AlbertForSequenceClassification` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertForSequenceClassification}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertForSequenceClassification = /// AlbertForSequenceClassification::new(&p.root(), &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertForSequenceClassification where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let albert = AlbertModel::new(p / "albert", config); let classifier_dropout_prob = match config.classifier_dropout_prob { Some(value) => value, None => 0.1, }; let dropout = Dropout::new(classifier_dropout_prob); let num_labels = config .id2label .as_ref() .expect("num_labels not provided in configuration") .len() as i64; let classifier = nn::linear( p / "classifier", config.hidden_size, num_labels, Default::default(), ); AlbertForSequenceClassification { albert, dropout, classifier, } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `output` - `Tensor` of shape (*batch size*, *num_labels*) /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertForSequenceClassification}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertForSequenceClassification = AlbertForSequenceClassification::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (output, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model /// .forward_t(Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false) /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) { let (_, pooled_output, all_hidden_states, all_attentions) = self .albert .forward_t( input_ids, mask, token_type_ids, position_ids, input_embeds, train, ) .unwrap(); let logits = pooled_output .apply_t(&self.dropout, train) .apply(&self.classifier); (logits, all_hidden_states, all_attentions) } } /// # ALBERT for token classification (e.g. NER, POS) /// Token-level classifier predicting a label for each token provided. Note that because of SentencePiece tokenization, the labels predicted are /// not necessarily aligned with words in the sentence. /// It is made of the following blocks: /// - `albert`: Base AlbertModel /// - `dropout`: Dropout to apply on the encoder last hidden states /// - `classifier`: Linear layer for token classification pub struct AlbertForTokenClassification { albert: AlbertModel, dropout: Dropout, classifier: nn::Linear, } impl AlbertForTokenClassification { /// Build a new `AlbertForTokenClassification` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertForTokenClassification}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertForTokenClassification = /// AlbertForTokenClassification::new(&p.root(), &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertForTokenClassification where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let albert = AlbertModel::new(p / "albert", config); let dropout = Dropout::new(config.hidden_dropout_prob); let num_labels = config .id2label .as_ref() .expect("num_labels not provided in configuration") .len() as i64; let classifier = nn::linear( p / "classifier", config.hidden_size, num_labels, Default::default(), ); AlbertForTokenClassification { albert, dropout, classifier, } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `output` - `Tensor` of shape (*batch size*, *sequence_length*, *num_labels*) containing the logits for each of the input tokens and classes /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertForTokenClassification}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertForTokenClassification = AlbertForTokenClassification::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (output, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model /// .forward_t(Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false) /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) { let (sequence_output, _, all_hidden_states, all_attentions) = self .albert .forward_t( input_ids, mask, token_type_ids, position_ids, input_embeds, train, ) .unwrap(); let logits = sequence_output .apply_t(&self.dropout, train) .apply(&self.classifier); (logits, all_hidden_states, all_attentions) } } /// # ALBERT for question answering /// Extractive question-answering model based on a ALBERT language model. Identifies the segment of a context that answers a provided question. /// Please note that a significant amount of pre- and post-processing is required to perform end-to-end question answering. /// See the question answering pipeline (also provided in this crate) for more details. /// It is made of the following blocks: /// - `albert`: Base AlbertModel /// - `qa_outputs`: Linear layer for question answering pub struct AlbertForQuestionAnswering { albert: AlbertModel, qa_outputs: nn::Linear, } impl AlbertForQuestionAnswering { /// Build a new `AlbertForQuestionAnswering` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertForQuestionAnswering}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertForQuestionAnswering = AlbertForQuestionAnswering::new(&p.root(), &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertForQuestionAnswering where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let albert = AlbertModel::new(p / "albert", config); let num_labels = 2; let qa_outputs = nn::linear( p / "qa_outputs", config.hidden_size, num_labels, Default::default(), ); AlbertForQuestionAnswering { albert, qa_outputs } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `start_scores` - `Tensor` of shape (*batch size*, *sequence_length*) containing the logits for start of the answer /// * `end_scores` - `Tensor` of shape (*batch size*, *sequence_length*) containing the logits for end of the answer /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertForQuestionAnswering}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertForQuestionAnswering = AlbertForQuestionAnswering::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (start_logits, end_logits, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model /// .forward_t(Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false) /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> ( Tensor, Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>, ) { let (sequence_output, _, all_hidden_states, all_attentions) = self .albert .forward_t( input_ids, mask, token_type_ids, position_ids, input_embeds, train, ) .unwrap(); let logits = sequence_output.apply(&self.qa_outputs).split(1, -1); let (start_logits, end_logits) = (&logits[0], &logits[1]); let start_logits = start_logits.squeeze1(-1); let end_logits = end_logits.squeeze1(-1); (start_logits, end_logits, all_hidden_states, all_attentions) } } /// # ALBERT for multiple choices /// Multiple choices model using a ALBERT base model and a linear classifier. /// Input should be in the form `[CLS] Context [SEP] Possible choice [SEP]`. The choice is made along the batch axis, /// assuming all elements of the batch are alternatives to be chosen from for a given context. /// It is made of the following blocks: /// - `albert`: Base AlbertModel /// - `dropout`: Dropout for hidden states output /// - `classifier`: Linear layer for multiple choices pub struct AlbertForMultipleChoice { albert: AlbertModel, dropout: Dropout, classifier: nn::Linear, } impl AlbertForMultipleChoice { /// Build a new `AlbertForMultipleChoice` /// /// # Arguments /// /// * `p` - Variable store path for the root of the ALBERT model /// * `config` - `AlbertConfig` object defining the model architecture and decoder status /// /// # Example /// /// ```no_run /// use rust_bert::albert::{AlbertConfig, AlbertForMultipleChoice}; /// use rust_bert::Config; /// use std::path::Path; /// use tch::{nn, Device}; /// /// let config_path = Path::new("path/to/config.json"); /// let device = Device::Cpu; /// let p = nn::VarStore::new(device); /// let config = AlbertConfig::from_file(config_path); /// let albert: AlbertForMultipleChoice = AlbertForMultipleChoice::new(&p.root(), &config); /// ``` pub fn new<'p, P>(p: P, config: &AlbertConfig) -> AlbertForMultipleChoice where P: Borrow<nn::Path<'p>>, { let p = p.borrow(); let albert = AlbertModel::new(p / "albert", config); let dropout = Dropout::new(config.hidden_dropout_prob); let num_labels = 1; let classifier = nn::linear( p / "classifier", config.hidden_size, num_labels, Default::default(), ); AlbertForMultipleChoice { albert, dropout, classifier, } } /// Forward pass through the model /// /// # Arguments /// /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see `input_embeds`) /// * `mask` - Optional mask of shape (*batch size*, *sequence_length*). Masked position have value 0, non-masked value 1. If None set to 1 /// * `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 /// /// * `output` - `Tensor` of shape (*1*, *batch size*) containing the logits for each of the alternatives given /// * `hidden_states` - `Option<Vec<Tensor>>` of length *num_hidden_layers* with shape (*batch size*, *sequence_length*, *hidden_size*) /// * `attentions` - `Option<Vec<Vec<Tensor>>>` of length *num_hidden_layers* of nested length *inner_group_num* with shape (*batch size*, *sequence_length*, *hidden_size*) /// /// # Example /// /// ```no_run /// # use tch::{nn, Device, Tensor, no_grad}; /// # use rust_bert::Config; /// # use std::path::Path; /// # use tch::kind::Kind::Int64; /// use rust_bert::albert::{AlbertConfig, AlbertForMultipleChoice}; /// # let config_path = Path::new("path/to/config.json"); /// # let device = Device::Cpu; /// # let vs = nn::VarStore::new(device); /// # let config = AlbertConfig::from_file(config_path); /// # let albert_model: AlbertForMultipleChoice = AlbertForMultipleChoice::new(&vs.root(), &config); /// let (batch_size, sequence_length) = (64, 128); /// let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); /// let mask = Tensor::zeros(&[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 (output, all_hidden_states, all_attentions) = no_grad(|| { /// albert_model /// .forward_t(Some(input_tensor), /// Some(mask), /// Some(token_type_ids), /// Some(position_ids), /// None, /// false).unwrap() /// }); /// ``` pub fn forward_t( &self, input_ids: Option<Tensor>, mask: Option<Tensor>, token_type_ids: Option<Tensor>, position_ids: Option<Tensor>, input_embeds: Option<Tensor>, train: bool, ) -> Result<(Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>), &'static str> { let (input_ids, input_embeds, num_choices) = match &input_ids { Some(input_value) => match &input_embeds { Some(_) => { return Err("Only one of input ids or input embeddings may be set"); } None => ( Some(input_value.view((-1, *input_value.size().last().unwrap()))), None, input_value.size()[1], ), }, None => match &input_embeds { Some(embeds) => ( None, Some(embeds.view((-1, embeds.size()[1], embeds.size()[2]))), embeds.size()[1], ), None => { return Err("At least one of input ids or input embeddings must be set"); } }, }; let mask = match mask { Some(value) => Some(value.view((-1, *value.size().last().unwrap()))), None => None, }; let token_type_ids = match token_type_ids { Some(value) => Some(value.view((-1, *value.size().last().unwrap()))), None => None, }; let position_ids = match position_ids { Some(value) => Some(value.view((-1, *value.size().last().unwrap()))), None => None, }; let (_, pooled_output, all_hidden_states, all_attentions) = self .albert .forward_t( input_ids, mask, token_type_ids, position_ids, input_embeds, train, ) .unwrap(); let logits = pooled_output .apply_t(&self.dropout, train) .apply(&self.classifier) .view((-1, num_choices)); Ok((logits, all_hidden_states, all_attentions)) } }