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#![allow(dead_code)]
// TODO: KV cache.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor, D};
use candle_hub::{api::sync::Api, Repo, RepoType};
use clap::Parser;
use rand::{distributions::Distribution, SeedableRng};
use tokenizers::Tokenizer;
mod model;
use model::{Config, Falcon, VarBuilder};
#[cfg(feature = "mkl")]
const DTYPE: DType = DType::F32;
#[cfg(not(feature = "mkl"))]
const DTYPE: DType = DType::BF16;
const TEMPERATURE: Option<f64> = None;
struct TextGeneration {
model: Falcon,
rng: rand::rngs::StdRng,
device: Device,
tokenizer: Tokenizer,
}
impl TextGeneration {
fn new(model: Falcon, tokenizer: Tokenizer, seed: u64, device: &Device) -> Self {
Self {
model,
tokenizer,
rng: rand::rngs::StdRng::seed_from_u64(seed),
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
println!("starting the inference loop");
let mut tokens = self
.tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let mut new_tokens = vec![];
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let start_gen = std::time::Instant::now();
let input = Tensor::new(tokens.as_slice(), &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input)?;
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let next_token = if let Some(temperature) = TEMPERATURE {
let prs = (&logits / temperature)?.softmax(D::Minus1)?;
let logits_v: Vec<f32> = prs.to_vec1()?;
let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
distr.sample(&mut self.rng) as u32
} else {
let logits_v: Vec<f32> = logits.to_vec1()?;
logits_v
.iter()
.enumerate()
.max_by(|(_, u), (_, v)| u.total_cmp(v))
.map(|(i, _)| i as u32)
.unwrap()
};
tokens.push(next_token);
new_tokens.push(next_token);
println!("> {:?}", start_gen.elapsed());
println!(
"{} token: {} '{}'",
index + 1,
next_token,
self.tokenizer
.decode(vec![next_token], true)
.map_err(E::msg)?
);
}
let dt = start_gen.elapsed();
println!(
"{sample_len} tokens generated ({} token/s)\n----\n{}\n----",
sample_len as f64 / dt.as_secs_f64(),
self.tokenizer.decode(new_tokens, true).map_err(E::msg)?
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(long)]
prompt: String,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "tiiuae/falcon-7b")]
model_id: String,
#[arg(long, default_value = "refs/pr/43")]
revision: String,
}
fn main() -> Result<()> {
let args = Args::parse();
let device = if args.cpu {
Device::Cpu
} else {
Device::new_cuda(0)?
};
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = Repo::with_revision(args.model_id, RepoType::Model, args.revision);
let tokenizer_filename = api.get(&repo, "tokenizer.json")?;
let mut filenames = vec![];
for rfilename in [
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
] {
let filename = api.get(&repo, rfilename)?;
filenames.push(filename);
}
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let weights = filenames
.iter()
.map(|f| Ok(unsafe { candle::safetensors::MmapedFile::new(f)? }))
.collect::<Result<Vec<_>>>()?;
let weights = weights
.iter()
.map(|f| Ok(f.deserialize()?))
.collect::<Result<Vec<_>>>()?;
let vb = VarBuilder::from_safetensors(weights, DTYPE, &device);
let config = Config::falcon7b();
config.validate()?;
let model = Falcon::load(&vb, config)?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(model, tokenizer, args.seed, &device);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
|
