rust-bert 0.23.0

Ready-to-use NLP pipelines and language models
Documentation 
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use rust_bert::gpt2::{
    GPT2LMHeadModel, Gpt2Config, Gpt2ConfigResources, Gpt2MergesResources, Gpt2ModelResources,
    Gpt2VocabResources,
};
use rust_bert::pipelines::generation_utils::Cache;
use rust_bert::resources::{RemoteResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::{Gpt2Tokenizer, Tokenizer, TruncationStrategy};
use tch::{nn, Device, Tensor};

#[test]
fn distilgpt2_lm_model() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Box::new(RemoteResource::from_pretrained(
        Gpt2ConfigResources::DISTIL_GPT2,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        Gpt2VocabResources::DISTIL_GPT2,
    ));
    let merges_resource = Box::new(RemoteResource::from_pretrained(
        Gpt2MergesResources::DISTIL_GPT2,
    ));
    let weights_resource = Box::new(RemoteResource::from_pretrained(
        Gpt2ModelResources::DISTIL_GPT2,
    ));
    let config_path = config_resource.get_local_path()?;
    let vocab_path = vocab_resource.get_local_path()?;
    let merges_path = merges_resource.get_local_path()?;
    let weights_path = weights_resource.get_local_path()?;

    //    Set-up model
    let device = Device::Cpu;
    let mut vs = nn::VarStore::new(device);
    let tokenizer: Gpt2Tokenizer = Gpt2Tokenizer::from_file(
        vocab_path.to_str().unwrap(),
        merges_path.to_str().unwrap(),
        false,
    )?;
    let config = Gpt2Config::from_file(config_path);
    let gpt2_model = GPT2LMHeadModel::new(vs.root(), &config);
    vs.load(weights_path)?;

    //    Define input
    let input = ["One two three four five six seven eight nine ten eleven"];
    let tokenized_input = tokenizer.encode_list(&input, 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::from_slice(&(input)))
        .collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

    //    Forward pass
    let model_output = gpt2_model
        .forward_t(Some(&input_tensor), None, None, None, None, None, false)
        .unwrap();

    let next_word_id = model_output
        .lm_logits
        .get(0)
        .get(-1)
        .argmax(-1, true)
        .int64_value(&[0]);
    let next_word = tokenizer.decode(&[next_word_id], true, true);

    assert_eq!(model_output.lm_logits.size(), vec!(1, 11, 50257));
    match model_output.cache {
        Cache::GPT2Cache(past) => {
            assert!(past.is_some());
            assert_eq!(past.as_ref().unwrap().len(), config.n_layer as usize);
            assert_eq!(
                past.as_ref().unwrap()[0].size(),
                vec!(2, 1, config.n_head, 11, 64)
            );
        }
        _ => panic!("Wrong cache returned for GPT2"),
    }
    assert!(
        (model_output.lm_logits.double_value(&[
            0,
            model_output.lm_logits.size()[1] - 1,
            next_word_id
        ]) - (-48.7065))
            .abs()
            < 1e-4
    );
    assert_eq!(next_word_id, 14104i64);
    assert_eq!(next_word, String::from(" twelve"));

    Ok(())
}