mirror of
https://github.com/guillaume-be/rust-bert.git
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76 lines
3.3 KiB
Rust
76 lines
3.3 KiB
Rust
// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
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// Copyright 2019 Guillaume Becquin
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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extern crate failure;
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use tch::{Device, nn, Tensor};
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use rust_tokenizers::{TruncationStrategy, Tokenizer, Gpt2Tokenizer};
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use rust_bert::gpt2::{Gpt2Config, GPT2LMHeadModel, Gpt2ConfigResources, Gpt2VocabResources, Gpt2MergesResources, Gpt2ModelResources};
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use rust_bert::pipelines::generation::{LMHeadModel, Cache};
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use rust_bert::resources::{Resource, download_resource, RemoteResource};
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use rust_bert::Config;
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fn main() -> failure::Fallible<()> {
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// Resources set-up
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let config_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let weights_resource = Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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let config_path = download_resource(&config_resource)?;
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let vocab_path = download_resource(&vocab_resource)?;
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let merges_path = download_resource(&merges_resource)?;
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let weights_path = download_resource(&weights_resource)?;
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// Set-up masked LM model
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let device = Device::Cpu;
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let mut vs = nn::VarStore::new(device);
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let tokenizer: Gpt2Tokenizer = Gpt2Tokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), false);
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let config = Gpt2Config::from_file(config_path);
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let gpt2_model = GPT2LMHeadModel::new(&vs.root(), &config);
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vs.load(weights_path)?;
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// Define input
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let input = ["One two three four five six seven eight nine ten eleven"];
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let tokenized_input = tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
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let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
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let tokenized_input = tokenized_input.
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iter().
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map(|input| input.token_ids.clone()).
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map(|mut input| {
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input.extend(vec![0; max_len - input.len()]);
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input
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}).
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map(|input|
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Tensor::of_slice(&(input))).
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collect::<Vec<_>>();
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let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);
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// Forward pass
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let (output, _, _, _, _) = gpt2_model.forward_t(
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&Some(input_tensor),
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Cache::None,
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&None,
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&None,
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&None,
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&None,
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None,
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&None,
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false).unwrap();
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let next_word_id = output.get(0).get(-1).argmax(-1, true).int64_value(&[0]);
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let next_word = tokenizer.decode(vec!(next_word_id), true, true);
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println!("Provided input: {}", input[0]);
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println!("Next word: {}", next_word);
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Ok(())
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} |