Add the Qwen2 model (#1684)
* Initial check-in for the qwen2 model. * More qwen2 inference. * Polish the qwen example. * Fix the rope basis. * Get the inference to work. * Support different model sizes.
This commit is contained in:
Laurent Mazare
GitHub
parent
0dee8ea19b
commit
5657e596cd
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@ -0,0 +1,281 @@
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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use anyhow::{Error as E, Result};
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use clap::Parser;
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use candle_transformers::models::qwen2::{Config, Model};
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use candle::{DType, Device, Tensor};
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use candle_examples::token_output_stream::TokenOutputStream;
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use candle_nn::VarBuilder;
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use candle_transformers::generation::LogitsProcessor;
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use hf_hub::{api::sync::Api, Repo, RepoType};
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use tokenizers::Tokenizer;
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struct TextGeneration {
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model: Model,
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device: Device,
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tokenizer: TokenOutputStream,
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logits_processor: LogitsProcessor,
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repeat_penalty: f32,
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repeat_last_n: usize,
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}
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impl TextGeneration {
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#[allow(clippy::too_many_arguments)]
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fn new(
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model: Model,
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tokenizer: Tokenizer,
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seed: u64,
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temp: Option<f64>,
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top_p: Option<f64>,
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repeat_penalty: f32,
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repeat_last_n: usize,
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device: &Device,
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) -> Self {
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let logits_processor = LogitsProcessor::new(seed, temp, top_p);
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Self {
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model,
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tokenizer: TokenOutputStream::new(tokenizer),
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logits_processor,
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repeat_penalty,
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repeat_last_n,
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device: device.clone(),
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}
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}
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fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
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use std::io::Write;
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self.tokenizer.clear();
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let mut tokens = self
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.tokenizer
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.tokenizer()
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.encode(prompt, true)
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.map_err(E::msg)?
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.get_ids()
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.to_vec();
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for &t in tokens.iter() {
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if let Some(t) = self.tokenizer.next_token(t)? {
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print!("{t}")
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}
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}
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std::io::stdout().flush()?;
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let mut generated_tokens = 0usize;
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let eos_token = match self.tokenizer.get_token("</s>") {
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Some(token) => token,
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None => anyhow::bail!("cannot find the </s> token"),
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};
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let start_gen = std::time::Instant::now();
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for index in 0..sample_len {
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let context_size = if index > 0 { 1 } else { tokens.len() };
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let start_pos = tokens.len().saturating_sub(context_size);
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let ctxt = &tokens[start_pos..];
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let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
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let logits = self.model.forward(&input, start_pos)?;
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let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
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let logits = if self.repeat_penalty == 1. {
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logits
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} else {
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let start_at = tokens.len().saturating_sub(self.repeat_last_n);
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candle_transformers::utils::apply_repeat_penalty(
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&logits,
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self.repeat_penalty,
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&tokens[start_at..],
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)?
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};
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let next_token = self.logits_processor.sample(&logits)?;
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tokens.push(next_token);
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generated_tokens += 1;
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if next_token == eos_token {
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break;
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}
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if let Some(t) = self.tokenizer.next_token(next_token)? {
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print!("{t}");
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std::io::stdout().flush()?;
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}
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}
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let dt = start_gen.elapsed();
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if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
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print!("{rest}");
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}
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std::io::stdout().flush()?;
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println!(
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"\n{generated_tokens} tokens generated ({:.2} token/s)",
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generated_tokens as f64 / dt.as_secs_f64(),
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);
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Ok(())
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}
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}
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#[derive(Clone, Copy, Debug, clap::ValueEnum, PartialEq, Eq)]
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enum WhichModel {
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#[value(name = "0.5b")]
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W0_5b,
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#[value(name = "1.8b")]
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W1_8b,
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#[value(name = "4b")]
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W4b,
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#[value(name = "7b")]
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W7b,
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#[value(name = "14b")]
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W14b,
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#[value(name = "72b")]
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W72b,
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}
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#[derive(Parser, Debug)]
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#[command(author, version, about, long_about = None)]
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struct Args {
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/// Run on CPU rather than on GPU.
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#[arg(long)]
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cpu: bool,
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/// Enable tracing (generates a trace-timestamp.json file).
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#[arg(long)]
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tracing: bool,
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#[arg(long)]
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use_flash_attn: bool,
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#[arg(long)]
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prompt: String,
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/// The temperature used to generate samples.
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#[arg(long)]
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temperature: Option<f64>,
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/// Nucleus sampling probability cutoff.
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#[arg(long)]
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top_p: Option<f64>,
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/// The seed to use when generating random samples.
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#[arg(long, default_value_t = 299792458)]
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seed: u64,
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/// The length of the sample to generate (in tokens).
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#[arg(long, short = 'n', default_value_t = 10000)]
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sample_len: usize,
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#[arg(long)]
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model_id: Option<String>,
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#[arg(long, default_value = "main")]
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revision: String,
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#[arg(long)]
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tokenizer_file: Option<String>,
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#[arg(long)]
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weight_files: Option<String>,
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/// Penalty to be applied for repeating tokens, 1. means no penalty.
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#[arg(long, default_value_t = 1.1)]
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repeat_penalty: f32,
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/// The context size to consider for the repeat penalty.
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#[arg(long, default_value_t = 64)]
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repeat_last_n: usize,
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#[arg(long, default_value = "0.5b")]
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model: WhichModel,
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}
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fn main() -> Result<()> {
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use tracing_chrome::ChromeLayerBuilder;
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use tracing_subscriber::prelude::*;
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let args = Args::parse();
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let _guard = if args.tracing {
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let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
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tracing_subscriber::registry().with(chrome_layer).init();
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Some(guard)
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} else {
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None
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};
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println!(
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"avx: {}, neon: {}, simd128: {}, f16c: {}",
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candle::utils::with_avx(),
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candle::utils::with_neon(),
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candle::utils::with_simd128(),
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candle::utils::with_f16c()
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);
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println!(
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"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
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args.temperature.unwrap_or(0.),
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args.repeat_penalty,
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args.repeat_last_n
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);
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let start = std::time::Instant::now();
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let api = Api::new()?;
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let model_id = match args.model_id {
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Some(model_id) => model_id,
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None => {
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let size = match args.model {
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WhichModel::W0_5b => "0.5B",
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WhichModel::W1_8b => "1.8B",
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WhichModel::W4b => "4B",
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WhichModel::W7b => "7B",
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WhichModel::W14b => "14B",
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WhichModel::W72b => "72B",
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};
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format!("Qwen/Qwen1.5-{size}")
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}
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};
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let repo = api.repo(Repo::with_revision(
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model_id,
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RepoType::Model,
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args.revision,
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));
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let tokenizer_filename = match args.tokenizer_file {
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Some(file) => std::path::PathBuf::from(file),
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None => repo.get("tokenizer.json")?,
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};
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let filenames = match args.weight_files {
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Some(files) => files
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.split(',')
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.map(std::path::PathBuf::from)
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.collect::<Vec<_>>(),
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None => match args.model {
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WhichModel::W0_5b | WhichModel::W1_8b => vec![repo.get("model.safetensors")?],
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WhichModel::W4b | WhichModel::W7b | WhichModel::W14b | WhichModel::W72b => {
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candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
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}
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},
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};
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println!("retrieved the files in {:?}", start.elapsed());
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let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
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let start = std::time::Instant::now();
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let config_file = repo.get("config.json")?;
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let config: Config = serde_json::from_slice(&std::fs::read(config_file)?)?;
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let device = candle_examples::device(args.cpu)?;
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let dtype = if device.is_cuda() {
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DType::BF16
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} else {
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DType::F32
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};
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
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let model = Model::new(&config, vb)?;
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println!("loaded the model in {:?}", start.elapsed());
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let mut pipeline = TextGeneration::new(
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model,
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tokenizer,
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args.seed,
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args.temperature,
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args.top_p,
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args.repeat_penalty,
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args.repeat_last_n,
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&device,
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);
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pipeline.run(&args.prompt, args.sample_len)?;
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Ok(())
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}
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@ -30,6 +30,7 @@ pub mod quantized_mixformer;
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pub mod quantized_mpt;
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pub mod quantized_stable_lm;
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pub mod quantized_t5;
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pub mod qwen2;
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pub mod repvgg;
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pub mod resnet;
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pub mod segment_anything;
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@ -0,0 +1,377 @@
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use crate::models::with_tracing::{linear, linear_no_bias, Linear};
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use candle::{DType, Device, Module, Result, Tensor, D};
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use candle_nn::{Activation, VarBuilder};
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use std::sync::Arc;
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#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
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pub struct Config {
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pub vocab_size: usize,
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pub hidden_size: usize,
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pub intermediate_size: usize,
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pub num_hidden_layers: usize,
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pub num_attention_heads: usize,
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pub num_key_value_heads: usize,
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pub max_position_embeddings: usize,
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pub sliding_window: usize,
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pub max_window_layers: usize,
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pub tie_word_embeddings: bool,
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pub rope_theta: f64,
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pub rms_norm_eps: f64,
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pub use_sliding_window: bool,
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pub hidden_act: Activation,
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}
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#[derive(Debug, Clone)]
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struct RmsNorm {
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inner: candle_nn::RmsNorm,
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span: tracing::Span,
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}
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impl RmsNorm {
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fn new(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
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let span = tracing::span!(tracing::Level::TRACE, "rms-norm");
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let inner = candle_nn::rms_norm(size, eps, vb)?;
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Ok(Self { inner, span })
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}
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}
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impl Module for RmsNorm {
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fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let _enter = self.span.enter();
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self.inner.forward(x)
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}
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}
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#[derive(Debug, Clone)]
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struct RotaryEmbedding {
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sin: Tensor,
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cos: Tensor,
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}
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fn rotate_half(xs: &Tensor) -> Result<Tensor> {
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let last_dim = xs.dim(D::Minus1)?;
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let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
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let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
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Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
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}
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impl RotaryEmbedding {
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fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
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let dim = cfg.hidden_size / cfg.num_attention_heads;
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let max_seq_len = cfg.max_position_embeddings;
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let inv_freq: Vec<_> = (0..dim)
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.step_by(2)
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.map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
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.collect();
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let inv_freq_len = inv_freq.len();
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let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
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let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
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.to_dtype(dtype)?
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.reshape((max_seq_len, 1))?;
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let freqs = t.matmul(&inv_freq)?;
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let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
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Ok(Self {
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sin: freqs.sin()?,
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cos: freqs.cos()?,
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})
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}
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fn apply_rotary_emb_qkv(
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&self,
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q: &Tensor,
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k: &Tensor,
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seqlen_offset: usize,
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) -> Result<(Tensor, Tensor)> {
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let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
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let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
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let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
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let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
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let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
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let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
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let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
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Ok((q_embed, k_embed))
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}
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}
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#[derive(Debug, Clone)]
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#[allow(clippy::upper_case_acronyms)]
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struct MLP {
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gate_proj: Linear,
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up_proj: Linear,
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down_proj: Linear,
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act_fn: Activation,
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}
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impl MLP {
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fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let hidden_sz = cfg.hidden_size;
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let intermediate_sz = cfg.intermediate_size;
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let gate_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("gate_proj"))?;
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let up_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("up_proj"))?;
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let down_proj = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("down_proj"))?;
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Ok(Self {
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gate_proj,
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up_proj,
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down_proj,
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act_fn: cfg.hidden_act,
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})
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}
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}
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impl Module for MLP {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
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let rhs = xs.apply(&self.up_proj)?;
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(lhs * rhs)?.apply(&self.down_proj)
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}
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}
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#[derive(Debug, Clone)]
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struct Attention {
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q_proj: Linear,
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k_proj: Linear,
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v_proj: Linear,
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o_proj: Linear,
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num_heads: usize,
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num_kv_heads: usize,
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num_kv_groups: usize,
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head_dim: usize,
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hidden_size: usize,
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rotary_emb: Arc<RotaryEmbedding>,
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kv_cache: Option<(Tensor, Tensor)>,
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}
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impl Attention {
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fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let hidden_sz = cfg.hidden_size;
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let num_heads = cfg.num_attention_heads;
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let num_kv_heads = cfg.num_key_value_heads;
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let num_kv_groups = num_heads / num_kv_heads;
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let head_dim = hidden_sz / num_heads;
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let q_proj = linear(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
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let k_proj = linear(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
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let v_proj = linear(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
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let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
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Ok(Self {
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q_proj,
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k_proj,
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v_proj,
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o_proj,
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num_heads,
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num_kv_heads,
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num_kv_groups,
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head_dim,
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hidden_size: hidden_sz,
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rotary_emb,
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||||
kv_cache: None,
|
||||
})
|
||||
}
|
||||
|
||||
fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
|
||||
let n_rep = self.num_kv_groups;
|
||||
if n_rep == 1 {
|
||||
Ok(xs)
|
||||
} else {
|
||||
let (b_sz, num_kv_heads, seq_len, head_dim) = xs.dims4()?;
|
||||
xs.unsqueeze(2)?
|
||||
.expand((b_sz, num_kv_heads, n_rep, seq_len, head_dim))?
|
||||
.reshape((b_sz, num_kv_heads * n_rep, seq_len, head_dim))
|
||||
}
|
||||
}
|
||||
|
||||
fn forward(
|
||||
&mut self,
|
||||
xs: &Tensor,
|
||||
attention_mask: Option<&Tensor>,
|
||||
seqlen_offset: usize,
|
||||
) -> Result<Tensor> {
|
||||
let (b_sz, q_len, _) = xs.dims3()?;
|
||||
|
||||
let query_states = self.q_proj.forward(xs)?;
|
||||
let key_states = self.k_proj.forward(xs)?;
|
||||
let value_states = self.v_proj.forward(xs)?;
|
||||
|
||||
let query_states = query_states
|
||||
.reshape((b_sz, q_len, self.num_heads, self.head_dim))?
|
||||
.transpose(1, 2)?;
|
||||
let key_states = key_states
|
||||
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
|
||||
.transpose(1, 2)?;
|
||||
let value_states = value_states
|
||||
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
|
||||
.transpose(1, 2)?;
|
||||
|
||||
let (query_states, key_states) =
|
||||
self.rotary_emb
|
||||
.apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
|
||||
|
||||
let (key_states, value_states) = match &self.kv_cache {
|
||||
None => (key_states, value_states),
|
||||
Some((prev_k, prev_v)) => {
|
||||
let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
|
||||
let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
|
||||
(key_states, value_states)
|
||||
}
|
||||
};
|
||||
self.kv_cache = Some((key_states.clone(), value_states.clone()));
|
||||
|
||||
let key_states = self.repeat_kv(key_states)?.contiguous()?;
|
||||
let value_states = self.repeat_kv(value_states)?.contiguous()?;
|
||||
|
||||
let attn_output = {
|
||||
let scale = 1f64 / f64::sqrt(self.head_dim as f64);
|
||||
let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
|
||||
|
||||
let attn_weights = match attention_mask {
|
||||
None => attn_weights,
|
||||
Some(mask) => attn_weights.broadcast_add(mask)?,
|
||||
};
|
||||
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
|
||||
attn_weights.matmul(&value_states)?
|
||||
};
|
||||
attn_output
|
||||
.transpose(1, 2)?
|
||||
.reshape((b_sz, q_len, self.hidden_size))?
|
||||
.apply(&self.o_proj)
|
||||
}
|
||||
|
||||
fn clear_kv_cache(&mut self) {
|
||||
self.kv_cache = None
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
struct DecoderLayer {
|
||||
self_attn: Attention,
|
||||
mlp: MLP,
|
||||
input_layernorm: RmsNorm,
|
||||
post_attention_layernorm: RmsNorm,
|
||||
}
|
||||
|
||||
impl DecoderLayer {
|
||||
fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
|
||||
let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
|
||||
let mlp = MLP::new(cfg, vb.pp("mlp"))?;
|
||||
let input_layernorm =
|
||||
RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
|
||||
let post_attention_layernorm = RmsNorm::new(
|
||||
cfg.hidden_size,
|
||||
cfg.rms_norm_eps,
|
||||
vb.pp("post_attention_layernorm"),
|
||||
)?;
|
||||
Ok(Self {
|
||||
self_attn,
|
||||
mlp,
|
||||
input_layernorm,
|
||||
post_attention_layernorm,
|
||||
})
|
||||
}
|
||||
|
||||
fn forward(
|
||||
&mut self,
|
||||
xs: &Tensor,
|
||||
attention_mask: Option<&Tensor>,
|
||||
seqlen_offset: usize,
|
||||
) -> Result<Tensor> {
|
||||
let residual = xs;
|
||||
let xs = self.input_layernorm.forward(xs)?;
|
||||
let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
|
||||
let xs = (xs + residual)?;
|
||||
let residual = &xs;
|
||||
let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
|
||||
residual + xs
|
||||
}
|
||||
|
||||
fn clear_kv_cache(&mut self) {
|
||||
self.self_attn.clear_kv_cache()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Model {
|
||||
embed_tokens: candle_nn::Embedding,
|
||||
layers: Vec<DecoderLayer>,
|
||||
norm: RmsNorm,
|
||||
lm_head: Linear,
|
||||
sliding_window: usize,
|
||||
device: Device,
|
||||
dtype: DType,
|
||||
}
|
||||
|
||||
impl Model {
|
||||
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
|
||||
let vb_m = vb.pp("model");
|
||||
let embed_tokens =
|
||||
candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
|
||||
let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
|
||||
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
|
||||
let vb_l = vb_m.pp("layers");
|
||||
for layer_idx in 0..cfg.num_hidden_layers {
|
||||
let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
|
||||
layers.push(layer)
|
||||
}
|
||||
let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
|
||||
let lm_head = linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
|
||||
Ok(Self {
|
||||
embed_tokens,
|
||||
layers,
|
||||
norm,
|
||||
lm_head,
|
||||
sliding_window: cfg.sliding_window,
|
||||
device: vb.device().clone(),
|
||||
dtype: vb.dtype(),
|
||||
})
|
||||
}
|
||||
|
||||
fn prepare_decoder_attention_mask(
|
||||
&self,
|
||||
b_size: usize,
|
||||
tgt_len: usize,
|
||||
seqlen_offset: usize,
|
||||
) -> Result<Tensor> {
|
||||
// Sliding window mask?
|
||||
let mask: Vec<_> = (0..tgt_len)
|
||||
.flat_map(|i| {
|
||||
(0..tgt_len).map(move |j| {
|
||||
if i < j || j + self.sliding_window < i {
|
||||
f32::NEG_INFINITY
|
||||
} else {
|
||||
0.
|
||||
}
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
|
||||
let mask = if seqlen_offset > 0 {
|
||||
let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
|
||||
Tensor::cat(&[&mask0, &mask], D::Minus1)?
|
||||
} else {
|
||||
mask
|
||||
};
|
||||
mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
|
||||
.to_dtype(self.dtype)
|
||||
}
|
||||
|
||||
pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
|
||||
let (b_size, seq_len) = input_ids.dims2()?;
|
||||
let attention_mask = if seq_len <= 1 {
|
||||
None
|
||||
} else {
|
||||
let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
|
||||
Some(mask)
|
||||
};
|
||||
let mut xs = self.embed_tokens.forward(input_ids)?;
|
||||
for layer in self.layers.iter_mut() {
|
||||
xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
|
||||
}
|
||||
xs.narrow(1, seq_len - 1, 1)?
|
||||
.apply(&self.norm)?
|
||||
.apply(&self.lm_head)
|
||||
}
|
||||
|
||||
pub fn clear_kv_cache(&mut self) {
|
||||
for layer in self.layers.iter_mut() {
|
||||
layer.clear_kv_cache()
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue