Struct rust_bert::models::gpt_neo::GptNeoModel

pub struct GptNeoModel { /* private fields */ }

GPT-Neo Base model

Base architecture for GPT-Neo models. Task-specific models will be built from this common base model It is made of the following blocks:

• word_embeddings: Word embeddings
• position_embeddings: Position embeddings
• layers: Vector of GptNeoBlock (transformer part of the model)

Implementations

impl GptNeoModel

pub fn new<'p, P>( p: P, config: &GptNeoConfig ) -> Result<GptNeoModel, RustBertError>

where P: Borrow<Path<'p>>,

Build a new GptNeoModel

Arguments

• p - Variable store path for the root of the GPT-Neo model
• config - GptNeoConfig object defining the model architecture

Example

use rust_bert::gpt_neo::{GptNeoConfig, GptNeoModel};
use rust_bert::Config;
use std::path::Path;
use tch::{nn, Device};

let config_path = Path::new("path/to/config.json");
let device = Device::Cpu;
let p = nn::VarStore::new(device);
let config = GptNeoConfig::from_file(config_path);
let gpt_neo_model = GptNeoModel::new(&p.root(), &config).unwrap();

pub fn forward_t( &self, input_ids: Option<&Tensor>, input_embeds: Option<&Tensor>, token_type_ids: Option<&Tensor>, position_ids: Option<&Tensor>, layer_states: Option<Vec<Option<LayerState>>>, attention_mask: Option<&Tensor>, train: bool ) -> Result<GptNeoModelOutput, RustBertError>

Forward pass through the model

Arguments

• input_ids - Optional input tensor of shape (batch size, sequence_length). This or input_embeds must be provided.
• input_embeds - Optional input tensor of shape (batch size, sequence_length, embeddings dimension). This or input_ids must be provided.
• token_type_ids - Optional token type ids used to indicate the portion of the input the token belongs to. If not None, token type embeddings will be added to the token and position embeddings.
• position_ids - Optional position ids of shape (batch size, sequence_length). If None, will be incremented starting from the length of the past input.
• layer_states - Optional Vector Option<Vec<Option<&LayerState>>> of length n_layer containing tuples with the past keys and values for both the self attention of each layer.
• attention_mask - Optional attention mask of shape (batch size, sequence_length) for the encoder positions. Positions with a mask with value 0 will be masked.
• train - boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.

Returns

• Result<GptNeoModelOutput, RustBertError> containing:
  • hidden_states - Tensor of shape (batch size, sequence_length, hidden_size) representing the activations of the last hidden state
  • next_cache - Option<Vec<Option<LayerState>>> of length n_layer containing the past content for the the attention layers
  • all_hidden_states - Option<Vec<Tensor>> of length n_layer + 1 with shape (batch size, sequence_length, hidden_size)
  • all_attentions - Option<Vec<Tensor>> of length n_layer containing the attention weights for each layer

Example

use rust_bert::gpt_neo::{GptNeoConfig, GptNeoModel};
let (batch_size, sequence_length) = (64, 128);
let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device));
let attention_mask = Tensor::ones(&[batch_size, sequence_length], (Int64, device));

let model_output = no_grad(|| {
    gpt_neo_model.forward_t(
        Some(&input_tensor),
        Some(&attention_mask),
        None,
        None,
        None,
        None,
        false,
    )
});