[−][src]Struct rust_bert::gpt2::GPT2LMHeadModel
GPT2 Language Modeling head
GPT2 model with a decoding head (linear layer without bias). The weights of the linear layer are tied to the word embeddings It is made of the following blocks:
transformer
: Base Gpt2Modellm_head
: Linear layer without bias tied to the weights of the token id embeddings
Implementations
impl GPT2LMHeadModel
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pub fn new(p: &Path, config: &Gpt2Config) -> GPT2LMHeadModel
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Build a new GPT2LMHeadModel
Arguments
p
- Variable store path for the root of the GPT2 modelconfig
-Gpt2Config
object defining the model architecture
Example
use tch::{nn, Device}; use rust_bert::Config; use std::path::Path; use rust_bert::gpt2::{Gpt2Config, GPT2LMHeadModel}; let config_path = Path::new("path/to/config.json"); let device = Device::Cpu; let p = nn::VarStore::new(device); let config = Gpt2Config::from_file(config_path); let gpt2: GPT2LMHeadModel = GPT2LMHeadModel::new(&(&p.root() / "gpt2"), &config);
Trait Implementations
impl LMHeadModel for GPT2LMHeadModel
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fn forward_t(
&mut self,
input_ids: &Option<Tensor>,
layer_past: &Option<Vec<Tensor>>,
attention_mask: &Option<Tensor>,
token_type_ids: &Option<Tensor>,
position_ids: &Option<Tensor>,
input_embeds: &Option<Tensor>,
_encoder_outputs: Option<&Tensor>,
_decoder_input_ids: &Option<Tensor>,
train: bool
) -> Result<(Tensor, Option<Tensor>, Option<Vec<Tensor>>, Option<Vec<Tensor>>, Option<Vec<Tensor>>), &'static str>
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&mut self,
input_ids: &Option<Tensor>,
layer_past: &Option<Vec<Tensor>>,
attention_mask: &Option<Tensor>,
token_type_ids: &Option<Tensor>,
position_ids: &Option<Tensor>,
input_embeds: &Option<Tensor>,
_encoder_outputs: Option<&Tensor>,
_decoder_input_ids: &Option<Tensor>,
train: bool
) -> Result<(Tensor, Option<Tensor>, Option<Vec<Tensor>>, Option<Vec<Tensor>>, Option<Vec<Tensor>>), &'static str>
Forward pass through the model
Arguments
input_ids
- Optional input tensor of shape (batch size, sequence_length). If None, pre-computed embeddings must be provided (seeinput_embeds
)layer_past
- Optional vector of size n_layer containing the past keys and values of each layer of shape (2, batch size, number of heads, past_sequence_length, hidden size per head). When provided, these are concatenated with the current input keys and values.attention_mask
- Optional mask of shape (batch size, sequence_length). Masked position have value 0, non-masked value 1. If None set to 1input_embeds
- Optional pre-computed input embeddings of shape (batch size, sequence_length, hidden_size). If None, input ids must be provided (seeinput_ids
)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._encoder_outputs
- Optional tensor of shape (batch size, source_sequence_length, encoder_hidden_dim). Unused for GPT2_decoder_input_ids
- Optional tensor of shape (batch size, target_sequence_length). Unused for GPT2train
- boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.
Returns
output
-Tensor
of shape (batch size, sequence_length, vocab_size) representing the logits for each vocab item and positionencoder_hidden_states
- Nonepast
-Option<Vec<Tensor>>
of length n_layer containing the past keys and values of each layer of shape (2, batch size, number of heads, past_sequence_length, hidden size per head)hidden_states
-Option<Vec<Tensor>>
of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)attentions
-Option<Vec<Tensor>>
of length num_hidden_layers with shape (batch size, sequence_length, hidden_size)
Example
use rust_bert::gpt2::{Gpt2Config, GPT2LMHeadModel}; use rust_bert::pipelines::generation::LMHeadModel; let (batch_size, sequence_length, past_sequence_length) = (64, 128, 56); let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device)); let mut past: Vec<Tensor> = Vec::with_capacity(config.n_layer as usize); for _ in 0..config.n_layer as usize { past.push(Tensor::rand(&[2, batch_size, config.n_head, past_sequence_length, config.n_embd / config.n_head], (Double, device))) } let attention_mask = Tensor::zeros(&[batch_size, sequence_length], (Int64, device)); let token_type_ids = Tensor::ones(&[batch_size, sequence_length], (Int64, device)); let position_ids = Tensor::arange(sequence_length, (Int64, device)).expand(&[batch_size, sequence_length], true); let (output, _, past, hidden_states, attentions) = no_grad(|| { gpt2_model .forward_t(&Some(input_tensor), &Some(past), &Some(attention_mask), &Some(token_type_ids), &Some(position_ids), &None, None, &None, false).unwrap() });
impl LanguageGenerator<GPT2LMHeadModel, Gpt2Vocab, Gpt2Tokenizer> for GPT2Generator
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Auto Trait Implementations
impl !RefUnwindSafe for GPT2LMHeadModel
impl !Send for GPT2LMHeadModel
impl !Sync for GPT2LMHeadModel
impl Unpin for GPT2LMHeadModel
impl !UnwindSafe for GPT2LMHeadModel
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,