Trait rust_bert::pipelines::generation_utils::LanguageGenerator[−][src]

pub trait LanguageGenerator<T: LMHeadModel, V: Vocab, U: Tokenizer<V>>: PrivateLanguageGenerator<T, V, U> {
    fn generate<S>(
        &self, 
        prompt_texts: Option<&[S]>, 
        generate_options: Option<GenerateOptions<'_>>
    ) -> Vec<GeneratedTextOutput>ⓘ
Notable traits for Vec<u8, A>
impl<A> Write for Vec<u8, A> where
    A: Allocator, 

    where
        S: AsRef<str> + Sync,
    { ... }

    fn generate_indices<S>(
        &self, 
        prompt_texts: Option<&[S]>, 
        generate_options: Option<GenerateOptions<'_>>
    ) -> Vec<GeneratedIndicesOutput>ⓘ
Notable traits for Vec<u8, A>
impl<A> Write for Vec<u8, A> where
    A: Allocator, 

    where
        S: AsRef<str> + Sync,
    { ... }

    fn generate_from_ids_and_past(
        &self, 
        input_ids: Tensor, 
        attention_mask: Option<Tensor>, 
        generate_options: Option<GenerateOptions<'_>>
    ) -> Vec<GeneratedIndicesOutput>ⓘ
Notable traits for Vec<u8, A>
impl<A> Write for Vec<u8, A> where
    A: Allocator, 
 { ... }

    fn get_tokenizer(&self) -> &TokenizerOption { ... }

    fn half(&mut self) { ... }

    fn float(&mut self) { ... }

    fn set_device(&mut self, device: Device) { ... }
}
Expand description

Common trait for text generation models.

Main API for text generation

Provided methods

Generate text based on a vector of promp texts.

Arguments
  • prompt_texts - Option<Vec<&str>> Optional vector of text prompts. An empty prompt to the model may be passed if the model implement a bos_id.
  • generate_options - Option<GenerateOptions> Optional set of generate options. If not (or partially) provided, will use the settings provided when creating the generator
Returns
  • Vec<TextOutput> Vector of length number_of_prompts x num_return_sequences containing TextOutput with the generated texts and the generation score if output_scores is true.
Example
use rust_bert::gpt2::GPT2Generator;
use rust_bert::pipelines::generation_utils::{
    GenerateConfig, GenerateOptions, LanguageGenerator,
};
use tch::Tensor;
let device = Device::cuda_if_available();
let generate_config = GenerateConfig {
    max_length: 30,
    do_sample: true,
    num_beams: 5,
    temperature: 1.1,
    num_return_sequences: 3,
    ..Default::default()
};
let gpt2_generator = GPT2Generator::new(generate_config)?;
let input_context = "The dog";
let second_input_context = "The cat was";

//Example custom function for fine-grained generation control
fn force_one_paragraph(_batch_id: i64, previous_token_ids: &Tensor) -> Vec<i64> {
    let paragraph_tokens = [198, 628];

    for paragraph_token in paragraph_tokens.iter() {
        if previous_token_ids
            .iter::<i64>()
            .unwrap()
            .collect::<Vec<i64>>()
            .contains(paragraph_token)
        {
            return vec![50256];
        }
    }
    (0..50255).collect()
}

let generate_options = GenerateOptions {
    min_length: Some(32),
    max_length: Some(128),
    output_scores: true,
    prefix_allowed_tokens_fn: Some(&force_one_paragraph),
    ..Default::default()
};

let output = gpt2_generator.generate(
    Some(&[input_context, second_input_context]),
    Some(generate_options),
);

Example output: 

[
    "The dog's owners, however, did not want to be named. According to the lawsuit, the animal's owner, a 29-year",
    "The dog has always been part of the family. \"He was always going to be my dog and he was always looking out for me",
    "The dog has been able to stay in the home for more than three months now. \"It's a very good dog. She's",
    "The cat was discovered earlier this month in the home of a relative of the deceased. The cat\'s owner, who wished to remain anonymous,",
    "The cat was pulled from the street by two-year-old Jazmine.\"I didn't know what to do,\" she said",
    "The cat was attacked by two stray dogs and was taken to a hospital. Two other cats were also injured in the attack and are being treated."
]

Generate token indices without decoding (useful for token-level operations before returning final text or as validation step during training).

Arguments
  • prompt_texts - Option<Vec<&str>> Optional vector of text prompts. An empty prompt to the model may be passed if the model implement a bos_id.
  • generate_options - Option<GenerateOptions> Optional set of generate options. If not (or partially) provided, will use the settings provided when creating the generator
Returns
  • Vec<IndicesOutput> Vector of length number_of_prompts x num_return_sequences containing IndicesOutput with the generated indices and the generation score if output_scores is true.
Example
use rust_bert::gpt2::GPT2Generator;
use rust_bert::pipelines::generation_utils::{
    GenerateConfig, GenerateOptions, LanguageGenerator,
};
use tch::Tensor;
let device = Device::cuda_if_available();
let generate_config = GenerateConfig {
    max_length: 30,
    do_sample: true,
    num_beams: 5,
    temperature: 1.1,
    num_return_sequences: 3,
    ..Default::default()
};
let gpt2_generator = GPT2Generator::new(generate_config)?;
let input_context = "The dog";
let second_input_context = "The cat was";

//Example custom function for fine-grained generation control
fn force_one_paragraph(_batch_id: i64, previous_token_ids: &Tensor) -> Vec<i64> {
    let paragraph_tokens = [198, 628];

    for paragraph_token in paragraph_tokens.iter() {
        if previous_token_ids
            .iter::<i64>()
            .unwrap()
            .collect::<Vec<i64>>()
            .contains(paragraph_token)
        {
            return vec![50256];
        }
    }
    (0..50255).collect()
}

let generate_options = GenerateOptions {
    min_length: Some(32),
    max_length: Some(128),
    output_scores: true,
    prefix_allowed_tokens_fn: Some(&force_one_paragraph),
    ..Default::default()
};

let output = gpt2_generator.generate_indices(
    Some(&[input_context, second_input_context]),
    Some(generate_options),
);

Generate token indices given a list of indices (useful when the input has been pre-tokenized). Returns a list of output tokens that need to be decoded using a tokenizer.

Arguments
  • input_ids - Tensor pre-tokenized and encoded input for generation.
  • generate_options - Option<GenerateOptions> Optional set of generate options. If not (or partially) provided, will use the settings provided when creating the generator
Returns
  • Vec<IndicesOutput> Vector of length number_of_prompts x num_return_sequences containing IndicesOutput with the generated indices and the generation score if output_scores is true.
Example
use rust_bert::gpt2::GPT2Generator;
use rust_bert::pipelines::generation_utils::{
    GenerateConfig, GenerateOptions, LanguageGenerator,
};
use tch::{Kind, Tensor};
let device = Device::cuda_if_available();

let gpt2_generator = GPT2Generator::new(Default::default())?;
let input_tensor = Tensor::randn(&[32, 128], (Kind::Int64, Device::Cpu));
let input_mask = Tensor::ones(&[32, 128], (Kind::Int64, Device::Cpu));

let generate_options = GenerateOptions {
    min_length: Some(32),
    max_length: Some(128),
    output_scores: true,
    ..Default::default()
};

let output = gpt2_generator.generate_from_ids_and_past(
    input_tensor,
    Some(input_mask),
    Some(generate_options),
);

Returns a reference to the text generator’s tokenizer

Returns
  • &TokenizerOption Reference to the generator’s tokenizer.
Example
use rust_bert::gpt2::GPT2Generator;
use rust_bert::pipelines::generation_utils::{GenerateConfig, LanguageGenerator};
use tch::Tensor;
let device = Device::cuda_if_available();
let generate_config = GenerateConfig {
    max_length: 30,
    do_sample: true,
    num_beams: 5,
    temperature: 1.1,
    num_return_sequences: 3,
    ..Default::default()
};
let gpt2_generator = GPT2Generator::new(generate_config)?;
let tokenizer = gpt2_generator.get_tokenizer();
tokenizer.tokenize("Hello, world!");

Implementors