electra_masked_lm.rs

// Copyright 2020 The Google Research Authors.
// Copyright 2019-present, the HuggingFace Inc. team
// Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
// Copyright 2019 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 rust_bert::electra::{
    ElectraConfig, ElectraConfigResources, ElectraForMaskedLM, ElectraModelResources,
    ElectraVocabResources,
};
use rust_bert::resources::{RemoteResource, Resource};
use rust_bert::Config;
use rust_tokenizers::{BertTokenizer, Tokenizer, TruncationStrategy, Vocab};
use tch::{nn, no_grad, Device, Tensor};

fn main() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(
        ElectraConfigResources::BASE_GENERATOR,
    ));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(
        ElectraVocabResources::BASE_GENERATOR,
    ));
    let weights_resource = Resource::Remote(RemoteResource::from_pretrained(
        ElectraModelResources::BASE_GENERATOR,
    ));
    let config_path = config_resource.get_local_path()?;
    let vocab_path = vocab_resource.get_local_path()?;
    let weights_path = weights_resource.get_local_path()?;

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

    //    Define input
    let input = [
        "Looks like one [MASK] is missing",
        "It was a very nice and [MASK] day",
    ];
    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 model_output =
        no_grad(|| electra_model.forward_t(Some(input_tensor), None, None, None, None, false));

    //    Print masked tokens
    let index_1 = model_output
        .prediction_scores
        .get(0)
        .get(4)
        .argmax(0, false);
    let index_2 = model_output
        .prediction_scores
        .get(1)
        .get(7)
        .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(&[]));

    println!("{}", word_1); // Outputs "thing" : "Looks like one [thing] is missing"
    println!("{}", word_2); // Outputs "sunny" : "It was a very nice and [sunny] day"

    Ok(())
}