use rust_bert::pipelines::common::{ModelResource, ModelType};
use rust_bert::pipelines::text_generation::{TextGenerationConfig, TextGenerationModel};
use rust_bert::reformer::{
ReformerConfig, ReformerConfigResources, ReformerForQuestionAnswering,
ReformerForSequenceClassification, ReformerModelResources, ReformerVocabResources,
};
use rust_bert::resources::{LocalResource, RemoteResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::{MultiThreadedTokenizer, ReformerTokenizer, TruncationStrategy};
use std::collections::HashMap;
use std::fs::File;
use std::io::BufReader;
use std::io::Write;
use tch::{nn, no_grad, Device, Tensor};
#[test]
fn test_generation_reformer() -> anyhow::Result<()> {
let config_resource = Box::new(RemoteResource::from_pretrained(
ReformerConfigResources::CRIME_AND_PUNISHMENT,
));
let original_config_path = config_resource.get_local_path()?;
let f = File::open(original_config_path).expect("Could not open configuration file.");
let br = BufReader::new(f);
let mut config: ReformerConfig =
serde_json::from_reader(br).expect("could not parse configuration");
config.hash_seed = Some(42);
let mut updated_config_file = tempfile::NamedTempFile::new()?;
let _ = updated_config_file.write_all(serde_json::to_string(&config).unwrap().as_bytes());
let updated_config_path = updated_config_file.into_temp_path();
let config_resource = Box::new(LocalResource {
local_path: updated_config_path.to_path_buf(),
});
let vocab_resource = Box::new(RemoteResource::from_pretrained(
ReformerVocabResources::CRIME_AND_PUNISHMENT,
));
let model_resource = Box::new(RemoteResource::from_pretrained(
ReformerModelResources::CRIME_AND_PUNISHMENT,
));
let generation_config = TextGenerationConfig {
model_type: ModelType::Reformer,
model_resource: ModelResource::Torch(model_resource),
config_resource,
vocab_resource,
merges_resource: None,
min_length: 100,
max_length: Some(100),
do_sample: false,
early_stopping: true,
no_repeat_ngram_size: 3,
num_beams: 3,
num_return_sequences: 1,
device: Device::Cpu,
..Default::default()
};
let model = TextGenerationModel::new(generation_config)?;
let input_context_1 = "The really great men must, I think,";
let input_context_2 = "It was a gloom winter night, and";
let output = model.generate(&[input_context_1, input_context_2], None)?;
assert_eq!(output.len(), 2);
assert_eq!(output[0], " The really great men must, I think, anyway waiting for some unknown reason, but Nikodim Fomitch and Ilya Petrovitch looked at him anguish invitable incidently at him. He could not resist an impression which might be setting");
assert_eq!(output[1], " It was a gloom winter night, and he went out into the street he remembered that he had turned to walked towards the Hay Market. Nastasya was going into a tavern-keeper. He was in the corner; he had come out of the win");
Ok(())
}
#[test]
fn reformer_for_sequence_classification() -> anyhow::Result<()> {
let config_resource = Box::new(RemoteResource::from_pretrained(
ReformerConfigResources::CRIME_AND_PUNISHMENT,
));
let vocab_resource = Box::new(RemoteResource::from_pretrained(
ReformerVocabResources::CRIME_AND_PUNISHMENT,
));
let config_path = config_resource.get_local_path()?;
let vocab_path = vocab_resource.get_local_path()?;
let device = Device::cuda_if_available();
let vs = nn::VarStore::new(device);
let tokenizer: ReformerTokenizer =
ReformerTokenizer::from_file(vocab_path.to_str().unwrap(), true)?;
let mut config = ReformerConfig::from_file(config_path);
let mut dummy_label_mapping = HashMap::new();
dummy_label_mapping.insert(0, String::from("Positive"));
dummy_label_mapping.insert(1, String::from("Negative"));
dummy_label_mapping.insert(3, String::from("Neutral"));
config.id2label = Some(dummy_label_mapping);
config.output_attentions = Some(true);
config.output_hidden_states = Some(true);
let reformer_model = ReformerForSequenceClassification::new(vs.root(), &config)?;
let input = [
"Looks like one thing is missing",
"It\'s like comparing oranges to apples",
];
let tokenized_input = tokenizer.encode_list(&input, 128, &TruncationStrategy::LongestFirst, 0);
let max_len = tokenized_input
.iter()
.map(|input| input.token_ids.len())
.max()
.unwrap();
let tokenized_input = tokenized_input
.iter()
.map(|input| input.token_ids.clone())
.map(|mut input| {
input.extend(vec![0; max_len - input.len()]);
input
})
.map(|input| Tensor::from_slice(&(input)))
.collect::<Vec<_>>();
let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);
let model_output =
no_grad(|| reformer_model.forward_t(Some(&input_tensor), None, None, None, None, false))?;
assert_eq!(model_output.logits.size(), &[2, 3]);
assert_eq!(
config.num_hidden_layers as usize,
model_output.all_hidden_states.unwrap().len()
);
assert_eq!(
config.num_hidden_layers as usize,
model_output.all_attentions.unwrap().len()
);
Ok(())
}
#[test]
fn reformer_for_question_answering() -> anyhow::Result<()> {
let config_resource = Box::new(RemoteResource::from_pretrained(
ReformerConfigResources::CRIME_AND_PUNISHMENT,
));
let vocab_resource = Box::new(RemoteResource::from_pretrained(
ReformerVocabResources::CRIME_AND_PUNISHMENT,
));
let config_path = config_resource.get_local_path()?;
let vocab_path = vocab_resource.get_local_path()?;
let device = Device::cuda_if_available();
let vs = nn::VarStore::new(device);
let tokenizer: ReformerTokenizer =
ReformerTokenizer::from_file(vocab_path.to_str().unwrap(), true)?;
let mut config = ReformerConfig::from_file(config_path);
config.output_attentions = Some(true);
config.output_hidden_states = Some(true);
let reformer_model = ReformerForQuestionAnswering::new(vs.root(), &config)?;
let input = [
"Looks like one thing is missing",
"It\'s like comparing oranges to apples",
];
let tokenized_input = tokenizer.encode_list(&input, 128, &TruncationStrategy::LongestFirst, 0);
let max_len = tokenized_input
.iter()
.map(|input| input.token_ids.len())
.max()
.unwrap();
let tokenized_input = tokenized_input
.iter()
.map(|input| input.token_ids.clone())
.map(|mut input| {
input.extend(vec![0; max_len - input.len()]);
input
})
.map(|input| Tensor::from_slice(&(input)))
.collect::<Vec<_>>();
let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);
let model_output =
no_grad(|| reformer_model.forward_t(Some(&input_tensor), None, None, None, None, false))?;
assert_eq!(model_output.start_logits.size(), &[2, 19]);
assert_eq!(model_output.end_logits.size(), &[2, 19]);
assert_eq!(
config.num_hidden_layers as usize,
model_output.all_hidden_states.unwrap().len()
);
assert_eq!(
config.num_hidden_layers as usize,
model_output.all_attentions.unwrap().len()
);
Ok(())
}