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Updated quantized phi model (#2099)
* Quantized phi in a separate file. * Add the quantized phi example + rework the model code. * Improve the phi model. * Get some generation out. * Use the appropriate rope shape. * Tweak the default prompt. --------- Co-authored-by: Jane Doe <jane.doe@example.org>
This commit is contained in:
Laurent Mazare
273
candle-examples/examples/quantized-phi/main.rs
Normal file
273
candle-examples/examples/quantized-phi/main.rs
Normal file
@ -0,0 +1,273 @@
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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 clap::{Parser, ValueEnum};
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use std::io::Write;
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use tokenizers::Tokenizer;
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use candle::quantized::gguf_file;
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use candle::Tensor;
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use candle_transformers::generation::{LogitsProcessor, Sampling};
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use candle_examples::token_output_stream::TokenOutputStream;
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use candle_transformers::models::quantized_phi as model;
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use model::ModelWeights;
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const DEFAULT_PROMPT: &str = "Write a function to count prime numbers up to N. ";
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#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
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enum Which {
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#[value(name = "phi-2")]
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Phi2,
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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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/// GGUF file to load, typically a .gguf file generated by the quantize command from llama.cpp
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#[arg(long)]
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model: Option<String>,
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/// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
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/// and 'chat' for an interactive model where history of previous prompts and generated tokens
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/// is preserved.
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#[arg(long)]
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prompt: Option<String>,
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/// The length of the sample to generate (in tokens).
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#[arg(short = 'n', long, default_value_t = 1000)]
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sample_len: usize,
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/// The tokenizer config in json format.
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#[arg(long)]
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tokenizer: Option<String>,
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/// The temperature used to generate samples, use 0 for greedy sampling.
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#[arg(long, default_value_t = 0.8)]
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temperature: 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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/// Only sample among the top K samples.
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#[arg(long)]
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top_k: Option<usize>,
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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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/// Enable tracing (generates a trace-timestamp.json file).
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#[arg(long)]
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tracing: bool,
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/// Process prompt elements separately.
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#[arg(long)]
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split_prompt: bool,
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/// Run on CPU rather than GPU even if a GPU is available.
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#[arg(long)]
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cpu: bool,
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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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/// The model size to use.
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#[arg(long, default_value = "phi-2")]
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which: Which,
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}
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impl Args {
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fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
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let tokenizer_path = match &self.tokenizer {
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Some(config) => std::path::PathBuf::from(config),
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None => {
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let api = hf_hub::api::sync::Api::new()?;
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let api = api.model("microsoft/phi-2".to_string());
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api.get("tokenizer.json")?
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}
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};
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Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)
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}
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fn model(&self) -> anyhow::Result<std::path::PathBuf> {
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let model_path = match &self.model {
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Some(config) => std::path::PathBuf::from(config),
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None => {
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let (repo, filename) = match self.which {
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Which::Phi2 => ("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf"),
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};
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let api = hf_hub::api::sync::Api::new()?;
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let api = api.model(repo.to_string());
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api.get(filename)?
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}
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};
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Ok(model_path)
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}
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}
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fn format_size(size_in_bytes: usize) -> String {
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if size_in_bytes < 1_000 {
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format!("{}B", size_in_bytes)
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} else if size_in_bytes < 1_000_000 {
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format!("{:.2}KB", size_in_bytes as f64 / 1e3)
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} else if size_in_bytes < 1_000_000_000 {
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format!("{:.2}MB", size_in_bytes as f64 / 1e6)
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} else {
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format!("{:.2}GB", size_in_bytes as f64 / 1e9)
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}
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}
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fn main() -> anyhow::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, args.repeat_penalty, args.repeat_last_n
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);
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let model_path = args.model()?;
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let mut file = std::fs::File::open(&model_path)?;
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let start = std::time::Instant::now();
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let device = candle_examples::device(args.cpu)?;
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let mut model = {
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let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
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let mut total_size_in_bytes = 0;
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for (_, tensor) in model.tensor_infos.iter() {
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let elem_count = tensor.shape.elem_count();
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total_size_in_bytes +=
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elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
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}
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println!(
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"loaded {:?} tensors ({}) in {:.2}s",
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model.tensor_infos.len(),
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&format_size(total_size_in_bytes),
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start.elapsed().as_secs_f32(),
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);
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ModelWeights::from_gguf(model, &mut file, &device)?
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};
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println!("model built");
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let tokenizer = args.tokenizer()?;
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let mut tos = TokenOutputStream::new(tokenizer);
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let prompt_str = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
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print!("{}", &prompt_str);
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let tokens = tos
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.tokenizer()
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.encode(prompt_str, true)
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.map_err(anyhow::Error::msg)?;
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let tokens = tokens.get_ids();
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let to_sample = args.sample_len.saturating_sub(1);
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let mut all_tokens = vec![];
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let mut logits_processor = {
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let temperature = args.temperature;
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let sampling = if temperature <= 0. {
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Sampling::ArgMax
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} else {
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match (args.top_k, args.top_p) {
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(None, None) => Sampling::All { temperature },
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(Some(k), None) => Sampling::TopK { k, temperature },
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(None, Some(p)) => Sampling::TopP { p, temperature },
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(Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
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}
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};
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LogitsProcessor::from_sampling(args.seed, sampling)
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};
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let start_prompt_processing = std::time::Instant::now();
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let mut next_token = if !args.split_prompt {
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let input = Tensor::new(tokens, &device)?.unsqueeze(0)?;
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let logits = model.forward(&input, 0)?;
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let logits = logits.squeeze(0)?;
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logits_processor.sample(&logits)?
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} else {
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let mut next_token = 0;
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for (pos, token) in tokens.iter().enumerate() {
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let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
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let logits = model.forward(&input, pos)?;
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let logits = logits.squeeze(0)?;
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next_token = logits_processor.sample(&logits)?
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}
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next_token
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};
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let prompt_dt = start_prompt_processing.elapsed();
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all_tokens.push(next_token);
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if let Some(t) = tos.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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let eos_token = *tos
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.tokenizer()
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.get_vocab(true)
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.get("<|endoftext|>")
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.unwrap();
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let start_post_prompt = std::time::Instant::now();
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let mut sampled = 0;
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for index in 0..to_sample {
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let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
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let logits = model.forward(&input, tokens.len() + index)?;
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let logits = logits.squeeze(0)?;
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let logits = if args.repeat_penalty == 1. {
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logits
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} else {
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let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
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candle_transformers::utils::apply_repeat_penalty(
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&logits,
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args.repeat_penalty,
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&all_tokens[start_at..],
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)?
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};
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next_token = logits_processor.sample(&logits)?;
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all_tokens.push(next_token);
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if let Some(t) = tos.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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sampled += 1;
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if next_token == eos_token {
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break;
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};
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}
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if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
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print!("{rest}");
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}
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std::io::stdout().flush()?;
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let dt = start_post_prompt.elapsed();
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println!(
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"\n\n{:4} prompt tokens processed: {:.2} token/s",
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tokens.len(),
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tokens.len() as f64 / prompt_dt.as_secs_f64(),
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);
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println!(
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"{sampled:4} tokens generated: {:.2} token/s",
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sampled as f64 / dt.as_secs_f64(),
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);
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Ok(())
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}
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@ -37,6 +37,7 @@ pub mod quantized_mistral;
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pub mod quantized_mixformer;
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pub mod quantized_moondream;
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pub mod quantized_mpt;
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pub mod quantized_phi;
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pub mod quantized_recurrent_gemma;
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pub mod quantized_rwkv_v5;
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pub mod quantized_rwkv_v6;
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288
candle-transformers/src/models/quantized_phi.rs
Normal file
288
candle-transformers/src/models/quantized_phi.rs
Normal file
@ -0,0 +1,288 @@
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use std::collections::HashMap;
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use candle::quantized::gguf_file;
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use candle::quantized::QTensor;
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use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
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use candle_nn::{Embedding, LayerNorm};
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pub const MAX_SEQ_LEN: usize = 4096;
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#[derive(Debug, Clone)]
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struct QLinear {
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inner: candle::quantized::QMatMul,
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bias: Tensor,
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span: tracing::Span,
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}
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impl QLinear {
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fn new<R: std::io::Read + std::io::Seek>(
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ct: &gguf_file::Content,
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r: &mut R,
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name: &str,
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device: &Device,
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) -> Result<Self> {
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let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
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let w = ct.tensor(r, &format!("{name}.weight"), device)?;
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let b = ct.tensor(r, &format!("{name}.bias"), device)?;
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let inner = candle::quantized::QMatMul::from_qtensor(w)?;
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let bias = b.dequantize(device)?;
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Ok(Self { inner, bias, span })
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}
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}
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impl Module for QLinear {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let _enter = self.span.enter();
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self.inner.forward(xs)?.broadcast_add(&self.bias)
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}
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}
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#[derive(Debug, Clone)]
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struct Mlp {
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ffn_up: QLinear,
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ffn_down: QLinear,
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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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xs.apply(&self.ffn_up)?.gelu()?.apply(&self.ffn_down)
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}
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}
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#[derive(Debug, Clone)]
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struct LayerWeights {
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attn_qkv: QLinear,
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attn_output: QLinear,
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attn_norm: LayerNorm,
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mlp: Mlp,
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n_head: usize,
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n_kv_head: usize,
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head_dim: usize,
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cos: Tensor,
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sin: Tensor,
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rope_dim: usize,
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neg_inf: Tensor,
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kv_cache: Option<(Tensor, Tensor)>,
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span_attn: tracing::Span,
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span_rot: tracing::Span,
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}
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fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: &Tensor) -> Result<Tensor> {
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let shape = mask.shape();
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let m = mask.where_cond(&on_true.broadcast_as(shape.dims())?, on_false)?;
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Ok(m)
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}
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impl LayerWeights {
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fn apply_rotary_emb(&self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
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let _enter = self.span_rot.enter();
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let (_b_sz, _n_head, seq_len, _n_embd) = xs.dims4()?;
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let xs_rot = xs.i((.., .., .., ..self.rope_dim))?;
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let xs_pass = xs.i((.., .., .., self.rope_dim..))?;
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let cos = self.cos.narrow(0, index_pos, seq_len)?;
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let sin = self.sin.narrow(0, index_pos, seq_len)?;
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let xs_rot = candle_nn::rotary_emb::rope(&xs_rot.contiguous()?, &cos, &sin)?;
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Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
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}
|
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|
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fn forward_attn(
|
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&mut self,
|
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x: &Tensor,
|
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mask: Option<&Tensor>,
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index_pos: usize,
|
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) -> Result<Tensor> {
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let _enter = self.span_attn.enter();
|
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let (b_sz, seq_len, n_embd) = x.dims3()?;
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let qkv =
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self.attn_qkv
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.forward(x)?
|
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.reshape((b_sz, seq_len, 3, self.n_head, self.head_dim))?;
|
||||
|
||||
let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
|
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let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
|
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let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
|
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// This call to contiguous ensures that the fast kernel can be called below. It's
|
||||
// actually a no-op except when processing the initial prompt so has no significant
|
||||
// impact on performance.
|
||||
let v = v.contiguous()?;
|
||||
|
||||
let q = self.apply_rotary_emb(&q, index_pos)?.contiguous()?;
|
||||
let k = self.apply_rotary_emb(&k, index_pos)?;
|
||||
|
||||
let (k, v) = match &self.kv_cache {
|
||||
None => (k.contiguous()?, v.contiguous()?),
|
||||
Some((k_cache, v_cache)) => {
|
||||
if index_pos == 0 {
|
||||
(k.contiguous()?, v.contiguous()?)
|
||||
} else {
|
||||
let k = Tensor::cat(&[k_cache, &k], 2)?;
|
||||
let v = Tensor::cat(&[v_cache, &v], 2)?;
|
||||
(k.contiguous()?, v.contiguous()?)
|
||||
}
|
||||
}
|
||||
};
|
||||
self.kv_cache = Some((k.clone(), v.clone()));
|
||||
|
||||
let k = crate::utils::repeat_kv(k, self.n_head / self.n_kv_head)?;
|
||||
let v = crate::utils::repeat_kv(v, self.n_head / self.n_kv_head)?;
|
||||
|
||||
let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
|
||||
let att = match mask {
|
||||
None => att,
|
||||
Some(mask) => {
|
||||
let mask = mask.broadcast_as(att.shape())?;
|
||||
masked_fill(&att, &mask, &self.neg_inf)?
|
||||
}
|
||||
};
|
||||
let att = candle_nn::ops::softmax_last_dim(&att)?;
|
||||
// Convert to contiguous as matmul doesn't support strided vs for now.
|
||||
let y = att.matmul(&v.contiguous()?)?;
|
||||
let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;
|
||||
let y = self.attn_output.forward(&y)?;
|
||||
Ok(y)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelWeights {
|
||||
tok_embeddings: Embedding,
|
||||
layers: Vec<LayerWeights>,
|
||||
output_norm: LayerNorm,
|
||||
output: QLinear,
|
||||
masks: HashMap<usize, Tensor>,
|
||||
span: tracing::Span,
|
||||
span_output: tracing::Span,
|
||||
}
|
||||
|
||||
fn precomput_freqs_cis(
|
||||
head_dim: usize,
|
||||
freq_base: f32,
|
||||
device: &Device,
|
||||
) -> Result<(Tensor, Tensor)> {
|
||||
let theta: Vec<_> = (0..head_dim)
|
||||
.step_by(2)
|
||||
.map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
|
||||
.collect();
|
||||
let theta = Tensor::new(theta.as_slice(), device)?;
|
||||
let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
|
||||
.to_dtype(DType::F32)?
|
||||
.reshape((MAX_SEQ_LEN, 1))?
|
||||
.matmul(&theta.reshape((1, theta.elem_count()))?)?;
|
||||
let cos = idx_theta.cos()?;
|
||||
let sin = idx_theta.sin()?;
|
||||
Ok((cos, sin))
|
||||
}
|
||||
|
||||
fn layer_norm(w: QTensor, b: QTensor, eps: f64) -> Result<LayerNorm> {
|
||||
let w = w.dequantize(&w.device())?;
|
||||
let b = b.dequantize(&b.device())?;
|
||||
let ln = LayerNorm::new(w, b, eps);
|
||||
Ok(ln)
|
||||
}
|
||||
|
||||
impl ModelWeights {
|
||||
pub fn from_gguf<R: std::io::Seek + std::io::Read>(
|
||||
ct: gguf_file::Content,
|
||||
reader: &mut R,
|
||||
device: &Device,
|
||||
) -> Result<Self> {
|
||||
let md_get = |s: &str| match ct.metadata.get(s) {
|
||||
None => candle::bail!("cannot find {s} in metadata"),
|
||||
Some(v) => Ok(v),
|
||||
};
|
||||
|
||||
// Parameter extraction from metadata.
|
||||
let head_count = md_get("phi2.attention.head_count")?.to_u32()? as usize;
|
||||
let head_count_kv = md_get("phi2.attention.head_count_kv")?.to_u32()? as usize;
|
||||
let block_count = md_get("phi2.block_count")?.to_u32()? as usize;
|
||||
let embedding_length = md_get("phi2.embedding_length")?.to_u32()? as usize;
|
||||
let rope_dim = md_get("phi2.rope.dimension_count")?.to_u32()? as usize;
|
||||
let ln_eps = md_get("phi2.attention.layer_norm_epsilon")?.to_f32()? as f64;
|
||||
let (cos, sin) = precomput_freqs_cis(rope_dim, 10_000., device)?;
|
||||
let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?;
|
||||
|
||||
let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?;
|
||||
let tok_embeddings = tok_embeddings.dequantize(device)?;
|
||||
let output_norm = layer_norm(
|
||||
ct.tensor(reader, "output_norm.weight", device)?,
|
||||
ct.tensor(reader, "output_norm.bias", device)?,
|
||||
ln_eps,
|
||||
)?;
|
||||
let output = QLinear::new(&ct, reader, "output", device)?;
|
||||
let mut layers = Vec::with_capacity(block_count);
|
||||
for layer_idx in 0..block_count {
|
||||
let prefix = format!("blk.{layer_idx}");
|
||||
let ffn_up = QLinear::new(&ct, reader, &format!("{prefix}.ffn_up"), device)?;
|
||||
let ffn_down = QLinear::new(&ct, reader, &format!("{prefix}.ffn_down"), device)?;
|
||||
let mlp = Mlp { ffn_up, ffn_down };
|
||||
let attn_norm = layer_norm(
|
||||
ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?,
|
||||
ct.tensor(reader, &format!("{prefix}.attn_norm.bias"), device)?,
|
||||
ln_eps,
|
||||
)?;
|
||||
let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
|
||||
let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
|
||||
layers.push(LayerWeights {
|
||||
attn_qkv: QLinear::new(&ct, reader, &format!("{prefix}.attn_qkv"), device)?,
|
||||
attn_output: QLinear::new(&ct, reader, &format!("{prefix}.attn_output"), device)?,
|
||||
attn_norm,
|
||||
mlp,
|
||||
n_head: head_count,
|
||||
n_kv_head: head_count_kv,
|
||||
head_dim: embedding_length / head_count,
|
||||
cos: cos.clone(),
|
||||
sin: sin.clone(),
|
||||
rope_dim,
|
||||
neg_inf: neg_inf.clone(),
|
||||
kv_cache: None,
|
||||
span_attn,
|
||||
span_rot,
|
||||
})
|
||||
}
|
||||
let span = tracing::span!(tracing::Level::TRACE, "model");
|
||||
let span_output = tracing::span!(tracing::Level::TRACE, "output");
|
||||
Ok(Self {
|
||||
tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
|
||||
layers,
|
||||
output_norm,
|
||||
output,
|
||||
masks: HashMap::new(),
|
||||
span,
|
||||
span_output,
|
||||
})
|
||||
}
|
||||
|
||||
fn mask(&mut self, t: usize, device: &Device) -> Result<Tensor> {
|
||||
if let Some(mask) = self.masks.get(&t) {
|
||||
Ok(mask.clone())
|
||||
} else {
|
||||
let mask: Vec<_> = (0..t)
|
||||
.flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
|
||||
.collect();
|
||||
let mask = Tensor::from_slice(&mask, (t, t), device)?;
|
||||
self.masks.insert(t, mask.clone());
|
||||
Ok(mask)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn forward(&mut self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
|
||||
let (_b_sz, seq_len) = xs.dims2()?;
|
||||
let mask = if seq_len == 1 {
|
||||
None
|
||||
} else {
|
||||
Some(self.mask(seq_len, xs.device())?)
|
||||
};
|
||||
let _enter = self.span.enter();
|
||||
let mut xs = self.tok_embeddings.forward(xs)?;
|
||||
for layer in self.layers.iter_mut() {
|
||||
let residual = &xs;
|
||||
let xs_norm = xs.apply(&layer.attn_norm)?;
|
||||
let attn_outputs = layer.forward_attn(&xs_norm, mask.as_ref(), index_pos)?;
|
||||
let feed_forward_hidden_states = layer.mlp.forward(&xs_norm)?;
|
||||
xs = (attn_outputs + feed_forward_hidden_states + residual)?
|
||||
}
|
||||
let xs = xs.apply(&self.output_norm)?.i((.., seq_len - 1, ..))?;
|
||||
let _enter = self.span_output.enter();
|
||||
self.output.forward(&xs)
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user