Module rust_bert::openai_gpt

Expand description

GPT (Radford et al.)

Implementation of the GPT2 language model (Improving Language Understanding by Generative Pre-Training Radford, Narasimhan, Salimans, Sutskever 2018). The base model is implemented in the openai_gpt_model::OpenAiGptModel struct. The model also includes a language model head: openai_gpt_model::OpenAIGPTLMHeadModel implementing the common generation_utils::LMHeadModel trait shared between the models used for generation (see pipelines for more information).

Model set-up and pre-trained weights loading

All models expect the following resources:

Configuration file expected to have a structure following the Transformers library
Model weights are expected to have a structure and parameter names following the Transformers library. A conversion using the Python utility scripts is required to convert the .bin weights to the .ot format.
GptTokenizer using a vocab.txt vocabulary and merges.txt 2-gram merges Pretrained models are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::gpt2::Gpt2Config;
use rust_bert::openai_gpt::OpenAiGptModel;
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::OpenAiGptTokenizer;

let config_resource = LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
};
let vocab_resource = LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
};
let merges_resource = LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
};
let weights_resource = LocalResource {
    local_path: PathBuf::from("path/to/model.ot"),
};
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()?;

let device = Device::cuda_if_available();
let mut vs = nn::VarStore::new(device);
let tokenizer: OpenAiGptTokenizer = OpenAiGptTokenizer::from_file(
    vocab_path.to_str().unwrap(),
    merges_path.to_str().unwrap(),
    true,
)?;
let config = Gpt2Config::from_file(config_path);
let gpt_model = OpenAiGptModel::new(&vs.root(), &config);
vs.load(weights_path)?;