use tch::{Device, nn, Tensor};
use rust_tokenizers::{Gpt2Tokenizer, TruncationStrategy, Tokenizer};
use rust_bert::Config;
use rust_bert::pipelines::generation::{GPT2Generator, LanguageGenerator, GenerateConfig, LMHeadModel};
use rust_bert::gpt2::{Gpt2Config, GPT2LMHeadModel, Gpt2ConfigResources, Gpt2MergesResources, Gpt2VocabResources, Gpt2ModelResources};
use rust_bert::resources::{RemoteResource, Resource, download_resource};
#[test]
fn gpt2_lm_model() -> failure::Fallible<()> {
let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
let weights_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
let config_path = download_resource(&config_resource)?;
let vocab_path = download_resource(&vocab_resource)?;
let merges_path = download_resource(&merges_resource)?;
let weights_path = download_resource(&weights_resource)?;
let device = Device::Cpu;
let mut vs = nn::VarStore::new(device);
let tokenizer: Gpt2Tokenizer = Gpt2Tokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), false);
let config = Gpt2Config::from_file(config_path);
let mut gpt2_model = GPT2LMHeadModel::new(&vs.root(), &config);
vs.load(weights_path)?;
let input = ["One two three four"];
let tokenized_input = tokenizer.encode_list(input.to_vec(), 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::of_slice(&(input))).
collect::<Vec<_>>();
let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);
let (output, _, past, _, _) = gpt2_model.forward_t(
&Some(input_tensor),
&None,
&None,
&None,
&None,
&None,
None,
&None,
false).unwrap();
let next_word_id = output.get(0).get(-1).argmax(-1, true).int64_value(&[0]);
let next_word = tokenizer.decode(vec!(next_word_id), true, true);
assert_eq!(output.size(), vec!(1, 4, 50257));
assert!(past.is_some());
assert_eq!(past.as_ref().unwrap().len(), config.n_layer as usize);
assert_eq!(past.as_ref().unwrap()[0].size(), vec!(2, 1, config.n_head, 4, 64));
assert!((output.double_value(&[0, output.size()[1] - 1, next_word_id]) - (-69.4948)).abs() < 1e-4);
assert_eq!(next_word_id, 1936i64);
assert_eq!(next_word, String::from(" five"));
Ok(())
}
#[test]
fn gpt2_generation_greedy() -> failure::Fallible<()> {
let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
let model_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
let generate_config = GenerateConfig {
model_resource,
config_resource,
vocab_resource,
merges_resource,
max_length: 40,
do_sample: false,
num_beams: 1,
temperature: 1.1,
repetition_penalty: 1.1,
..Default::default()
};
let mut model = GPT2Generator::new(generate_config)?;
let input_context = "The cat";
let output = model.generate(Some(vec!(input_context)), None);
assert_eq!(output.len(), 1);
assert_eq!(output[0], "The cat was found in a field near the town of Keflavik, about 30 miles (48 kilometers) south-east of Moscow.\n\n\n");
Ok(())
}
#[test]
fn gpt2_generation_beam_search() -> failure::Fallible<()> {
let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
let model_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
let generate_config = GenerateConfig {
model_resource,
config_resource,
vocab_resource,
merges_resource,
max_length: 20,
do_sample: false,
num_beams: 5,
temperature: 1.2,
num_return_sequences: 3,
..Default::default()
};
let mut model = GPT2Generator::new(generate_config)?;
let input_context = "The dog";
let output = model.generate(Some(vec!(input_context)), None);
assert_eq!(output.len(), 3);
assert_eq!(output[0], "The dog was found in the backyard of a home in the 6200 block of South Main Street.");
assert_eq!(output[1], "The dog was found in the backyard of a home in the 6500 block of South Main Street.");
assert_eq!(output[2], "The dog was found in the backyard of a home in the 6200 block of South Main Street,");
Ok(())
}
#[test]
fn gpt2_generation_beam_search_multiple_prompts_without_padding() -> failure::Fallible<()> {
let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
let model_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
let generate_config = GenerateConfig {
model_resource,
config_resource,
vocab_resource,
merges_resource,
max_length: 20,
do_sample: false,
num_beams: 5,
temperature: 1.2,
num_return_sequences: 3,
..Default::default()
};
let mut model = GPT2Generator::new(generate_config)?;
let input_context_1 = "The dog";
let input_context_2 = "The cat";
let output = model.generate(Some(vec!(input_context_1, input_context_2)), None);
assert_eq!(output.len(), 6);
assert_eq!(output[0], "The dog was found in the backyard of a home in the 6200 block of South Main Street.");
assert_eq!(output[1], "The dog was found in the backyard of a home in the 6500 block of South Main Street.");
assert_eq!(output[2], "The dog was found in the backyard of a home in the 6200 block of South Main Street,");
assert_eq!(output[3], "The cat-and-mouse game.\n\n\"I think it\'s going to be interesting to");
assert_eq!(output[4], "The cat-and-mouse game.\n\n\"I think it\'s going to be a very");
assert_eq!(output[5], "The cat-and-mouse game.\n\n\"I think it\'s going to be very interesting");
Ok(())
}
#[test]
fn gpt2_generation_beam_search_multiple_prompts_with_padding() -> failure::Fallible<()> {
let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
let model_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
let generate_config = GenerateConfig {
model_resource,
config_resource,
vocab_resource,
merges_resource,
max_length: 20,
do_sample: false,
num_beams: 5,
temperature: 1.2,
num_return_sequences: 3,
..Default::default()
};
let mut model = GPT2Generator::new(generate_config)?;
let input_context_1 = "The dog";
let input_context_2 = "The cat was";
let output = model.generate(Some(vec!(input_context_1, input_context_2)), None);
assert_eq!(output.len(), 6);
assert_eq!(output[0], "The dog was found dead on the side of the road in the middle of the night.\n");
assert_eq!(output[1], "The dog was found dead on the side of the road in the middle of the night on Sunday");
assert_eq!(output[2], "The dog was found dead on the side of the road in the middle of the night on Saturday");
assert_eq!(output[3], "The cat was taken to a local hospital, where it was treated and released.\n\nPolice said");
assert_eq!(output[4], "The cat was taken to a local hospital, where it was treated and released.\n\n\"It");
assert_eq!(output[5], "The cat was taken to a local hospital, where it was treated and released.\n\n\"We");
Ok(())
}