pub struct T5Model { /* private fields */ }
Expand description
§T5 Base model
Base architecture for T5 model. Usually complemented with a task-specific head, such as a language model head. It is made of the following blocks:
encoder
:T5Stack
(transformer) made of a vector of encoding layersdecoder
:T5Stack
(transformer) made of a vector of decoding layers with self attention and encoder cross-attention. caching is implemented for the decoder to avoid recalculating static states (encoder key/values and previously calculated decoder key/values)embeddings
:nn::Embedding
Shared embeddings for the encoder and decoder.
Implementations§
Source§impl T5Model
impl T5Model
Sourcepub fn new<'p, P>(p: P, config: &T5Config) -> T5Model
pub fn new<'p, P>(p: P, config: &T5Config) -> T5Model
Build a new T5Model
§Arguments
p
- Variable store path for the root of the T5 modelconfig
-T5Config
object defining the model architecture
§Example
use rust_bert::t5::{T5Config, T5Model};
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 = T5Config::from_file(config_path);
let t5: T5Model = T5Model::new(&p.root() / "t5", &config);
Sourcepub fn forward_t(
&self,
input_ids: Option<&Tensor>,
attention_mask: Option<&Tensor>,
encoder_outputs: Option<&Tensor>,
decoder_input_ids: Option<&Tensor>,
decoder_attention_mask: Option<&Tensor>,
input_embeds: Option<&Tensor>,
decoder_input_embeds: Option<&Tensor>,
old_layer_states: Option<Vec<(Option<LayerState>, Option<LayerState>)>>,
train: bool,
) -> T5ModelOutput
pub fn forward_t( &self, input_ids: Option<&Tensor>, attention_mask: Option<&Tensor>, encoder_outputs: Option<&Tensor>, decoder_input_ids: Option<&Tensor>, decoder_attention_mask: Option<&Tensor>, input_embeds: Option<&Tensor>, decoder_input_embeds: Option<&Tensor>, old_layer_states: Option<Vec<(Option<LayerState>, Option<LayerState>)>>, train: bool, ) -> T5ModelOutput
Forward pass through the model
§Arguments
input_ids
- Optional input tensor of shape (batch size, source_sequence_length). This orinput_embeds
must be provided.attention_mask
- Optional attention mask of shape (batch size, source_sequence_length) for the encoder positions. Positions with a mask with value 0 will be masked.decoder_input_ids
- Optional input tensor of shape (batch size, target_sequence_length). This ordecoder_input_embeds
must be provided.encoder_outputs
- Optional tuple made of a tensor of shape (batch size, source_sequence_length, encoder_hidden_dim) and optional vectors of tensors of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size). These correspond to the encoder last hidden state and optional hidden states/attention weights for encoder layers. When provided, the encoder hidden state will not be recalculated. Useful for generation tasks.decoder_attention_mask
- Optional attention mask of shape (batch size, target_sequence_length) for the decoder positions. Positions with a mask with value 0 will be masked.input_embeds
- Optional input tensor of shape (batch size, source_sequence_length, embeddings dimension). This orinput_ids
must be provided.decoder_input_embeds
- Optional input tensor of shape (batch size, target_sequence_length, embeddings dimension). This ordecoder_input_ids
must be provided.old_layer_states
- Optional vector of lengthnum_layers
containing tuples of optionalLayerStates
containing the last calculated key and value pairs for the decoder. This avoids recomputing attention weights at past positions and speeds up decoding.train
- boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.
§Returns
T5ModelOutput
containing:decoder_output
-Tensor
of shape (batch size, target_sequence_length, hidden_size) representing the activations of the last decoder hidden stateencoder_hidden_states
-Tensor
of shape (batch size, source_sequence_length, hidden_size) representing the activations of the last encoder hidden statecache
-Option<Vec<(Option<Vec<LayerState, LayerState>>)>>
of length n_layer containing the encoder padding mask and past keys and values for both the self attention and the encoder cross attention of each layer of the decoder.all_encoder_hidden_states
-Option<Vec<Tensor>>
of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size)all_encoder_attentions
-Option<Vec<Tensor>>
of length num_encoder_layers with shape (batch size, source_sequence_length, hidden_size)all_decoder_hidden_states
-Option<Vec<Tensor>>
of length num_decoder_layers with shape (batch size, target_sequence_length, hidden_size)all_decoder_attentions
-Option<Vec<Tensor>>
of length num_decoder_layers with shape (batch size, target_sequence_length, hidden_size)
§Example
use rust_bert::t5::{T5Config, T5Model};
let (batch_size, source_sequence_length, target_sequence_length) = (64, 128, 56);
let input_tensor = Tensor::rand(&[batch_size, source_sequence_length], (Int64, device));
let target_tensor = Tensor::rand(&[batch_size, target_sequence_length], (Int64, device));
let encoder_attention_mask =
Tensor::ones(&[batch_size, source_sequence_length], (Int64, device));
let decoder_attention_mask =
Tensor::ones(&[batch_size, source_sequence_length], (Int64, device));
let model_output = no_grad(|| {
t5_model.forward_t(
Some(&input_tensor),
Some(&encoder_attention_mask),
None,
Some(&target_tensor),
Some(&decoder_attention_mask),
None,
None,
None,
false,
)
});
Auto Trait Implementations§
impl Freeze for T5Model
impl RefUnwindSafe for T5Model
impl Send for T5Model
impl !Sync for T5Model
impl Unpin for T5Model
impl UnwindSafe for T5Model
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is true
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