rust-bert 0.7.2

use tch::{Device, nn, Tensor, no_grad};
use rust_tokenizers::{RobertaTokenizer, TruncationStrategy, Tokenizer, Vocab};
use rust_bert::Config;
use rust_bert::bert::BertConfig;
use rust_bert::roberta::{RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForMultipleChoice, RobertaForTokenClassification, RobertaForQuestionAnswering, RobertaConfigResources, RobertaVocabResources, RobertaMergesResources, RobertaModelResources};
use std::collections::HashMap;
use rust_bert::resources::{RemoteResource, Resource, download_resource};

#[test]
fn roberta_masked_lm() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaConfigResources::ROBERTA));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaVocabResources::ROBERTA));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaMergesResources::ROBERTA));
    let weights_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaModelResources::ROBERTA));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_resource)?;
    let merges_path = download_resource(&merges_resource)?;
    let weights_path = download_resource(&weights_resource)?;

//    Set-up masked LM model
    let device = Device::Cpu;
    let mut vs = nn::VarStore::new(device);
    let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
    let config = BertConfig::from_file(config_path);
    let roberta_model = RobertaForMaskedLM::new(&vs.root(), &config);
    vs.load(weights_path)?;

//    Define input
    let input = ["<pad> Looks like one thing is missing", "It\'s like comparing oranges to apples"];
    let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
    let mut tokenized_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        collect::<Vec<_>>();

//    Masking the token [thing] of sentence 1 and [oranges] of sentence 2
    tokenized_input[0][4] = 103;
    tokenized_input[1][5] = 103;
    let tokenized_input = tokenized_input.
        iter().
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

//    Forward pass
    let (output, _, _) = no_grad(|| {
        roberta_model
            .forward_t(Some(input_tensor),
                       None,
                       None,
                       None,
                       None,
                       &None,
                       &None,
                       false)
    });

//    Print masked tokens
    let index_1 = output.get(0).get(4).argmax(0, false);
    let index_2 = output.get(1).get(5).argmax(0, false);
    let word_1 = tokenizer.vocab().id_to_token(&index_1.int64_value(&[]));
    let word_2 = tokenizer.vocab().id_to_token(&index_2.int64_value(&[]));

    assert_eq!("Ġsome", word_1); // Outputs "person" : "Looks like [some] thing is missing"
    assert_eq!("Ġapples", word_2);// Outputs "pear" : "It\'s like comparing [apples] to apples"

    Ok(())
}

#[test]
fn roberta_for_sequence_classification() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaConfigResources::ROBERTA));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaVocabResources::ROBERTA));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaMergesResources::ROBERTA));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_resource)?;
    let merges_path = download_resource(&merges_resource)?;

//    Set-up model
    let device = Device::Cpu;
    let vs = nn::VarStore::new(device);
    let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
    let mut config = BertConfig::from_file(config_path);
    let mut dummy_label_mapping = HashMap::new();
    dummy_label_mapping.insert(0, String::from("Positive"));
    dummy_label_mapping.insert(1, String::from("Negative"));
    dummy_label_mapping.insert(3, String::from("Neutral"));
    config.id2label = Some(dummy_label_mapping);
    config.output_attentions = Some(true);
    config.output_hidden_states = Some(true);
    let roberta_model = RobertaForSequenceClassification::new(&vs.root(), &config);


//    Define input
    let input = ["Looks like one thing is missing", "It\'s like comparing oranges to apples"];
    let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
    let tokenized_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

//    Forward pass
    let (output, all_hidden_states, all_attentions) = no_grad(|| {
        roberta_model
            .forward_t(Some(input_tensor),
                       None,
                       None,
                       None,
                       None,
                       false)
    });

    assert_eq!(output.size(), &[2, 3]);
    assert_eq!(config.num_hidden_layers as usize, all_hidden_states.unwrap().len());
    assert_eq!(config.num_hidden_layers as usize, all_attentions.unwrap().len());

    Ok(())
}

#[test]
fn roberta_for_multiple_choice() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaConfigResources::ROBERTA));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaVocabResources::ROBERTA));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaMergesResources::ROBERTA));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_resource)?;
    let merges_path = download_resource(&merges_resource)?;

//    Set-up model
    let device = Device::Cpu;
    let vs = nn::VarStore::new(device);
    let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
    let mut config = BertConfig::from_file(config_path);
    config.output_attentions = Some(true);
    config.output_hidden_states = Some(true);
    let roberta_model = RobertaForMultipleChoice::new(&vs.root(), &config);


//    Define input
    let input = ["Looks like one thing is missing", "It\'s like comparing oranges to apples"];
    let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
    let tokenized_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device).unsqueeze(0);

//    Forward pass
    let (output, all_hidden_states, all_attentions) = no_grad(|| {
        roberta_model
            .forward_t(input_tensor,
                       None,
                       None,
                       None,
                       false)
    });

    assert_eq!(output.size(), &[1, 2]);
    assert_eq!(config.num_hidden_layers as usize, all_hidden_states.unwrap().len());
    assert_eq!(config.num_hidden_layers as usize, all_attentions.unwrap().len());

    Ok(())
}

#[test]
fn roberta_for_token_classification() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaConfigResources::ROBERTA));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaVocabResources::ROBERTA));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaMergesResources::ROBERTA));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_resource)?;
    let merges_path = download_resource(&merges_resource)?;

//    Set-up model
    let device = Device::Cpu;
    let vs = nn::VarStore::new(device);
    let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
    let mut config = BertConfig::from_file(config_path);
    let mut dummy_label_mapping = HashMap::new();
    dummy_label_mapping.insert(0, String::from("O"));
    dummy_label_mapping.insert(1, String::from("LOC"));
    dummy_label_mapping.insert(2, String::from("PER"));
    dummy_label_mapping.insert(3, String::from("ORG"));
    config.id2label = Some(dummy_label_mapping);
    config.output_attentions = Some(true);
    config.output_hidden_states = Some(true);
    let roberta_model = RobertaForTokenClassification::new(&vs.root(), &config);

//    Define input
    let input = ["Looks like one thing is missing", "It\'s like comparing oranges to apples"];
    let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
    let tokenized_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

//    Forward pass
    let (output, all_hidden_states, all_attentions) = no_grad(|| {
        roberta_model
            .forward_t(Some(input_tensor),
                       None,
                       None,
                       None,
                       None,
                       false)
    });

    assert_eq!(output.size(), &[2, 9, 4]);
    assert_eq!(config.num_hidden_layers as usize, all_hidden_states.unwrap().len());
    assert_eq!(config.num_hidden_layers as usize, all_attentions.unwrap().len());

    Ok(())
}


#[test]
fn roberta_for_question_answering() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaConfigResources::ROBERTA));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaVocabResources::ROBERTA));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(RobertaMergesResources::ROBERTA));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_resource)?;
    let merges_path = download_resource(&merges_resource)?;

//    Set-up model
    let device = Device::Cpu;
    let vs = nn::VarStore::new(device);
    let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
    let mut config = BertConfig::from_file(config_path);
    let mut dummy_label_mapping = HashMap::new();
    dummy_label_mapping.insert(0, String::from("Positive"));
    dummy_label_mapping.insert(1, String::from("Negative"));
    dummy_label_mapping.insert(3, String::from("Neutral"));
    config.id2label = Some(dummy_label_mapping);
    config.output_attentions = Some(true);
    config.output_hidden_states = Some(true);
    let roberta_model = RobertaForQuestionAnswering::new(&vs.root(), &config);


//    Define input
    let input = ["Looks like one thing is missing", "It\'s like comparing oranges to apples"];
    let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
    let tokenized_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

//    Forward pass
    let (start_scores, end_scores, all_hidden_states, all_attentions) = no_grad(|| {
        roberta_model
            .forward_t(Some(input_tensor),
                       None,
                       None,
                       None,
                       None,
                       false)
    });

    assert_eq!(start_scores.size(), &[2, 9]);
    assert_eq!(end_scores.size(), &[2, 9]);
    assert_eq!(config.num_hidden_layers as usize, all_hidden_states.unwrap().len());
    assert_eq!(config.num_hidden_layers as usize, all_attentions.unwrap().len());

    Ok(())
}