mirror of
https://github.com/guillaume-be/rust-bert.git
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1f4d344668
* - Changed the return type of generate method to be `Result`, removed fallible unwraps * Fix doctests
100 lines
3.3 KiB
Rust
100 lines
3.3 KiB
Rust
use std::path::PathBuf;
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use rust_bert::gpt_j::{GptJConfigResources, GptJMergesResources, GptJVocabResources};
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use rust_bert::pipelines::common::{ModelResource, ModelType};
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use rust_bert::pipelines::text_generation::{TextGenerationConfig, TextGenerationModel};
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use rust_bert::resources::{LocalResource, RemoteResource};
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use tch::Device;
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/// Equivalent Python code:
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///
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/// ```python
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/// import torch
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/// from transformers import AutoTokenizer, GPTJForCausalLM
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///
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/// device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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///
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/// model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16).to(device)
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///
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/// tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", padding_side="left")
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/// tokenizer.add_special_tokens({"pad_token": tokenizer.eos_token})
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///
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/// prompts = ["It was a very nice and sunny", "It was a gloom winter night, and"]
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/// inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).to(device)
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///
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/// with torch.no_grad():
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/// gen_tokens = model.generate(
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/// **inputs,
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/// min_length=0,
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/// max_length=32,
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/// do_sample=False,
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/// early_stopping=True,
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/// num_beams=1,
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/// num_return_sequences=1
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/// )
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///
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/// gen_texts = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
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/// ````
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///
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/// To run this test you need to download `pytorch_model.bin` from [EleutherAI GPT-J 6B
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/// (float16)][gpt-j-6B-float16] and then convert its weights with:
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///
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/// ```
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/// python utils/convert_model.py resources/gpt-j-6B-float16/pytorch_model.bin
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/// ```
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///
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/// [gpt-j-6B-float16]: https://huggingface.co/EleutherAI/gpt-j-6B/tree/float16
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fn main() -> anyhow::Result<()> {
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// Resources paths
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let config_resource = Box::new(RemoteResource::from_pretrained(
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GptJConfigResources::GPT_J_6B_FLOAT16,
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));
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let vocab_resource = Box::new(RemoteResource::from_pretrained(
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GptJVocabResources::GPT_J_6B_FLOAT16,
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));
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let merges_resource = Box::new(RemoteResource::from_pretrained(
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GptJMergesResources::GPT_J_6B_FLOAT16,
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));
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let model_resource = Box::new(LocalResource::from(PathBuf::from(
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"resources/gpt-j-6B-float16/rust_model.ot",
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)));
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// Set-up model
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let generation_config = TextGenerationConfig {
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model_type: ModelType::GPTJ,
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model_resource: ModelResource::Torch(model_resource),
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config_resource,
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vocab_resource,
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merges_resource: Some(merges_resource),
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min_length: 10,
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max_length: Some(32),
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do_sample: false,
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early_stopping: true,
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num_beams: 1,
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num_return_sequences: 1,
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device: Device::cuda_if_available(),
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..Default::default()
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};
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let model = TextGenerationModel::new(generation_config)?;
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// Generate text
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let prompts = [
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"It was a very nice and sunny",
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"It was a gloom winter night, and",
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];
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let output = model.generate(&prompts, None)?;
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assert_eq!(output.len(), 2);
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assert_eq!(output[0], "It was a very nice and sunny day, and I was sitting in the garden of my house, enjoying the sun and the fresh air. I was thinking");
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assert_eq!(output[1], "It was a gloom winter night, and the wind was howling. The snow was falling, and the temperature was dropping. The snow was coming down so hard");
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Ok(())
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}
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