Quantized version of mistral. (#1009)

* Quantized version of mistral.

* Integrate the quantized mistral variant.

* Use the quantized weight files.

* Tweak the quantization command.

* Fix the dtype when computing the rotary embeddings.

* Update the readme with the quantized version.

* Fix the decoding of the remaining tokens.
This commit is contained in:
Laurent Mazare
2023-09-30 19:25:47 +02:00
committed by GitHub
parent 06207332bc
commit deee7612da
7 changed files with 507 additions and 37 deletions

View File

@ -7,7 +7,8 @@ extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mistral::{Config, Model};
use candle_transformers::models::mistral::{Config, Model as Mistral};
use candle_transformers::models::quantized_mistral::Model as QMistral;
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
@ -16,6 +17,11 @@ use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
Mistral(Mistral),
Quantized(QMistral),
}
struct TextGeneration {
model: Model,
device: Device,
@ -76,7 +82,10 @@ impl TextGeneration {
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input, start_pos)?;
let logits = match &mut self.model {
Model::Mistral(m) => m.forward(&input, start_pos)?,
Model::Quantized(m) => m.forward(&input, start_pos)?,
};
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
@ -101,8 +110,9 @@ impl TextGeneration {
}
}
let dt = start_gen.elapsed();
let rest = self.tokenizer.decode_rest().map_err(E::msg)?;
print!("{rest}");
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
@ -211,24 +221,39 @@ fn main() -> Result<()> {
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => vec![
repo.get("pytorch_model-00001-of-00002.safetensors")?,
repo.get("pytorch_model-00002-of-00002.safetensors")?,
],
None => {
if args.quantized {
vec![repo.get("model-q4k.gguf")?]
} else {
vec![
repo.get("pytorch_model-00001-of-00002.safetensors")?,
repo.get("pytorch_model-00002-of-00002.safetensors")?,
]
}
}
};
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 config = Config::config_7b_v0_1(args.use_flash_attn);
let device = candle_examples::device(args.cpu)?;
let dtype = if device.is_cuda() {
DType::BF16
let (model, device) = if args.quantized {
let filename = &filenames[0];
let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
let model = QMistral::new(&config, vb)?;
(Model::Quantized(model), Device::Cpu)
} else {
DType::F32
let device = candle_examples::device(args.cpu)?;
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Mistral::new(&config, vb)?;
(Model::Mistral(model), device)
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Model::new(&config, vb)?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(