albert_loading_inference_loop.rs

albert_loading_inference_loop.rs

// 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.

extern crate anyhow;

use rust_bert::albert::{
    AlbertConfig, AlbertConfigResources, AlbertForMaskedLM, AlbertModelResources,
    AlbertVocabResources,
};
use rust_bert::resources::{RemoteResource, Resource};
use rust_bert::Config;
use rust_tokenizers::tokenizer::{AlbertTokenizer, Tokenizer, TruncationStrategy};
use rust_tokenizers::vocab::Vocab;
use tch::{nn, no_grad, Device, Tensor, Kind};

fn main() -> anyhow::Result<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(
        AlbertConfigResources::ALBERT_BASE_V2,
    ));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(
        AlbertVocabResources::ALBERT_BASE_V2,
    ));
    let weights_resource = Resource::Remote(RemoteResource::from_pretrained(
        AlbertModelResources::ALBERT_BASE_V2,
    ));
    let config_path = config_resource.get_local_path()?;
    let vocab_path = vocab_resource.get_local_path()?;
    let weights_path = weights_resource.get_local_path()?;

    let mut i = 0;
    loop {
        //    Set-up masked LM model
        let device = Device::cuda_if_available();
        let mut vs = nn::VarStore::new(device);
        let config = AlbertConfig::from_file(&config_path);
        let albert_model = AlbertForMaskedLM::new(&vs.root(), &config);
        vs.load(&weights_path)?;

        let input_tensor = Tensor::randint(10, &[2, 10], (Kind::Int64, device));
        // //    Forward pass
        let model_output =
            no_grad(|| albert_model.forward_t(Some(input_tensor), None, None, None, None, false));
        println!(
            "{:?}",
            model_output.prediction_scores.double_value(&[0, 0, 0])
        );
       i += 1;
        println!("iteration {}", i);
    }
    Ok(())
}