mirror of
https://github.com/huggingface/candle.git
synced 2025-06-18 19:47:12 +00:00
Move the llama2-c model in transformers. (#1205)
This commit is contained in:
Laurent Mazare
@ -6,10 +6,10 @@ extern crate accelerate_src;
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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mod model;
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mod qmodel;
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use candle_transformers::models::llama2_c as model;
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use candle_transformers::models::llama2_c_weights as weights;
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use candle_transformers::models::quantized_llama2_c as qmodel;
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mod training;
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mod weights;
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use clap::{Parser, Subcommand};
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use anyhow::{Error as E, Result};
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@ -1,314 +0,0 @@
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use candle::{DType, Device, IndexOp, Result, Tensor, D};
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use candle_nn::linear_no_bias as linear;
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use candle_nn::{embedding, rms_norm, Embedding, Linear, Module, RmsNorm, VarBuilder};
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use std::collections::HashMap;
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use std::sync::{Arc, Mutex};
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#[derive(Debug, Clone)]
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pub struct Config {
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pub dim: usize, // transformer dimension
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pub hidden_dim: usize, // for ffn layers
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pub n_layers: usize, // number of layers
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pub n_heads: usize, // number of query heads
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pub n_kv_heads: usize, // number of key/value heads (can be < query heads because of multiquery)
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pub vocab_size: usize, // vocabulary size, usually 256 (byte-level)
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pub seq_len: usize, // max sequence length
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pub norm_eps: f64,
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}
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impl Config {
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pub fn tiny() -> Self {
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Self {
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dim: 288,
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hidden_dim: 768,
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n_layers: 6,
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n_heads: 6,
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n_kv_heads: 6,
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vocab_size: 32000,
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seq_len: 256,
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norm_eps: 1e-5,
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}
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}
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}
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#[derive(Clone)]
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pub struct Cache {
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masks: Arc<Mutex<HashMap<usize, Tensor>>>,
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pub use_kv_cache: bool,
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#[allow(clippy::type_complexity)]
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pub kvs: Arc<Mutex<Vec<Option<(Tensor, Tensor)>>>>,
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pub cos: Tensor,
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pub sin: Tensor,
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device: Device,
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}
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impl Cache {
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pub fn new(use_kv_cache: bool, cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let n_elem = cfg.dim / cfg.n_heads;
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let theta: Vec<_> = (0..n_elem)
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.step_by(2)
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.map(|i| 1f32 / 10000f32.powf(i as f32 / n_elem as f32))
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.collect();
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let theta = Tensor::new(theta.as_slice(), vb.device())?;
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let idx_theta = Tensor::arange(0, cfg.seq_len as u32, vb.device())?
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.to_dtype(DType::F32)?
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.reshape((cfg.seq_len, 1))?
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.matmul(&theta.reshape((1, theta.elem_count()))?)?;
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let precomputed_cos = idx_theta.cos()?;
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let precomputed_sin = idx_theta.sin()?;
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let freq_cis_real = vb
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.get((cfg.seq_len, cfg.head_size() / 2), "freq_cis_real")
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.unwrap_or(precomputed_cos);
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let freq_cis_imag = vb
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.get((cfg.seq_len, cfg.head_size() / 2), "freq_cis_imag")
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.unwrap_or(precomputed_sin);
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let cos = freq_cis_real.reshape((cfg.seq_len, cfg.head_size() / 2, 1))?;
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let sin = freq_cis_imag.reshape((cfg.seq_len, cfg.head_size() / 2, 1))?;
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Ok(Self {
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masks: Arc::new(Mutex::new(HashMap::new())),
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use_kv_cache,
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kvs: Arc::new(Mutex::new(vec![None; cfg.n_layers])),
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cos,
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sin,
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device: vb.device().clone(),
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})
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}
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pub fn mask(&self, t: usize) -> Result<Tensor> {
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let mut masks = self.masks.lock().unwrap();
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if let Some(mask) = masks.get(&t) {
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Ok(mask.clone())
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} else {
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let mask: Vec<_> = (0..t)
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.flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
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.collect();
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let mask = Tensor::from_slice(&mask, (t, t), &self.device)?;
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masks.insert(t, mask.clone());
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Ok(mask)
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}
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}
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}
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fn silu(xs: &Tensor) -> Result<Tensor> {
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xs / (xs.neg()?.exp()? + 1.0)?
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}
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struct CausalSelfAttention {
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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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n_head: usize,
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n_key_value_head: usize,
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head_dim: usize,
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cache: Cache,
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}
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impl CausalSelfAttention {
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fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
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let (b_sz, seq_len, h, n_embd) = x.dims4()?;
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let cos = self.cache.cos.i(index_pos..index_pos + seq_len)?;
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let sin = self.cache.sin.i(index_pos..index_pos + seq_len)?;
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let cos = cos.unsqueeze(1)?;
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let sin = sin.unsqueeze(1)?;
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let cos = cos.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
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let sin = sin.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
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let x = x.reshape((b_sz, seq_len, h, n_embd / 2, 2))?;
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let x0 = x.narrow(D::Minus1, 0, 1)?;
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let x1 = x.narrow(D::Minus1, 1, 1)?;
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let dst0 = (x0.broadcast_mul(&cos)? - x1.broadcast_mul(&sin)?)?;
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let dst1 = (x0.broadcast_mul(&sin)? + x1.broadcast_mul(&cos)?)?;
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let rope = Tensor::cat(&[&dst0, &dst1], D::Minus1)?.reshape((b_sz, seq_len, h, n_embd))?;
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Ok(rope)
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}
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fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
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let (b_sz, seq_len, n_embd) = x.dims3()?;
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let q = self.q_proj.forward(x)?;
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let k = self.k_proj.forward(x)?;
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let v = self.v_proj.forward(x)?;
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let q = q.reshape((b_sz, seq_len, self.n_head, self.head_dim))?;
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let k = k.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
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let mut v = v.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
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let q = self.apply_rotary_emb(&q, index_pos)?;
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let mut k = self.apply_rotary_emb(&k, index_pos)?;
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if self.cache.use_kv_cache {
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let mut cache = self.cache.kvs.lock().unwrap();
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if let Some((cache_k, cache_v)) = &cache[block_idx] {
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k = Tensor::cat(&[cache_k, &k], 1)?.contiguous()?;
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v = Tensor::cat(&[cache_v, &v], 1)?.contiguous()?;
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}
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cache[block_idx] = Some((k.clone(), v.clone()))
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}
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let k = self.repeat_kv(k)?;
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let v = self.repeat_kv(v)?;
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let q = q.transpose(1, 2)?.contiguous()?;
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let k = k.transpose(1, 2)?.contiguous()?;
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let v = v.transpose(1, 2)?.contiguous()?;
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let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
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let mask = self.cache.mask(seq_len)?.broadcast_as(att.shape())?;
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let att = masked_fill(&att, &mask, f32::NEG_INFINITY)?;
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let att = candle_nn::ops::softmax(&att, D::Minus1)?;
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// Convert to contiguous as matmul doesn't support strided vs for now.
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let y = att.matmul(&v.contiguous()?)?;
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let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;
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let y = self.o_proj.forward(&y)?;
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Ok(y)
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}
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fn repeat_kv(&self, x: Tensor) -> Result<Tensor> {
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let n_rep = self.n_head / self.n_key_value_head;
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if n_rep == 1 {
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Ok(x)
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} else {
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let (b_sz, seq_len, n_kv_head, head_dim) = x.dims4()?;
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let x = x
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.unsqueeze(3)?
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.expand((b_sz, seq_len, n_kv_head, n_rep, head_dim))?
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.reshape((b_sz, seq_len, n_kv_head * n_rep, head_dim))?;
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Ok(x)
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}
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}
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fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
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let size_in = cfg.dim;
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let size_q = (cfg.dim / cfg.n_heads) * cfg.n_heads;
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let size_kv = (cfg.dim / cfg.n_heads) * cfg.n_kv_heads;
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let q_proj = linear(size_in, size_q, vb.pp("q_proj"))?;
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let k_proj = linear(size_in, size_kv, vb.pp("k_proj"))?;
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let v_proj = linear(size_in, size_kv, vb.pp("v_proj"))?;
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let o_proj = linear(size_q, size_in, 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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n_head: cfg.n_heads,
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n_key_value_head: cfg.n_kv_heads,
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head_dim: cfg.dim / cfg.n_heads,
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cache: cache.clone(),
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})
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}
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}
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fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
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let shape = mask.shape();
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let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
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let m = mask.where_cond(&on_true, on_false)?;
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Ok(m)
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}
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struct Mlp {
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c_fc1: Linear,
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c_fc2: Linear,
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c_proj: Linear,
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}
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impl Mlp {
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fn new(c_fc1: Linear, c_fc2: Linear, c_proj: Linear) -> Self {
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Self {
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c_fc1,
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c_fc2,
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c_proj,
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}
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}
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fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let x = (silu(&self.c_fc1.forward(x)?)? * self.c_fc2.forward(x)?)?;
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self.c_proj.forward(&x)
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}
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fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
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let h_size = cfg.dim;
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let i_size = cfg.hidden_dim;
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let c_fc1 = linear(h_size, i_size, vb.pp("gate_proj"))?;
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let c_fc2 = linear(h_size, i_size, vb.pp("up_proj"))?;
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let c_proj = linear(i_size, h_size, vb.pp("down_proj"))?;
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Ok(Self::new(c_fc1, c_fc2, c_proj))
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}
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}
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struct Block {
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rms_1: RmsNorm,
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attn: CausalSelfAttention,
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rms_2: RmsNorm,
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mlp: Mlp,
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}
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impl Block {
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fn new(rms_1: RmsNorm, attn: CausalSelfAttention, rms_2: RmsNorm, mlp: Mlp) -> Self {
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Self {
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rms_1,
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attn,
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rms_2,
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mlp,
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}
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}
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fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
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let residual = x;
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let x = self.rms_1.forward(x)?;
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let x = (self.attn.forward(&x, index_pos, block_idx)? + residual)?;
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let residual = &x;
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let x = (self.mlp.forward(&self.rms_2.forward(&x)?)? + residual)?;
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Ok(x)
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}
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fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
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let attn = CausalSelfAttention::load(vb.pp("self_attn"), cache, cfg)?;
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let mlp = Mlp::load(vb.pp("mlp"), cfg)?;
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let input_layernorm = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("input_layernorm"))?;
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let post_attention_layernorm =
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rms_norm(cfg.dim, cfg.norm_eps, vb.pp("post_attention_layernorm"))?;
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Ok(Self::new(
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input_layernorm,
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attn,
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post_attention_layernorm,
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mlp,
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))
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}
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}
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pub struct Llama {
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wte: Embedding,
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blocks: Vec<Block>,
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ln_f: RmsNorm,
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lm_head: Linear,
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pub config: Config,
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}
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impl Llama {
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pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
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let (_b_sz, _seq_len) = x.dims2()?;
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let mut x = self.wte.forward(x)?;
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for (block_idx, block) in self.blocks.iter().enumerate() {
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x = block.forward(&x, index_pos, block_idx)?;
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}
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let x = self.ln_f.forward(&x)?;
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let logits = self.lm_head.forward(&x)?;
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logits.to_dtype(DType::F32)
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}
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pub fn load(vb: VarBuilder, cache: &Cache, cfg: Config) -> Result<Self> {
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let wte = embedding(cfg.vocab_size, cfg.dim, vb.pp("model.embed_tokens"))?;
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let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
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let ln_f = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
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let blocks: Vec<_> = (0..cfg.n_layers)
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.map(|i| Block::load(vb.pp(&format!("model.layers.{i}")), cache, &cfg).unwrap())
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.collect();
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Ok(Self {
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wte,
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blocks,
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ln_f,
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lm_head,
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config: cfg,
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})
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}
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}
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@ -1,227 +0,0 @@
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use super::model::{Cache, Config};
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use candle::{DType, IndexOp, Module, Result, Tensor, D};
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use candle_transformers::quantized_nn::{linear_no_bias as linear, Embedding, Linear, RmsNorm};
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pub use candle_transformers::quantized_var_builder::VarBuilder;
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fn silu(xs: &Tensor) -> Result<Tensor> {
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xs / (xs.neg()?.exp()? + 1.0)?
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}
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struct CausalSelfAttention {
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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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n_head: usize,
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n_key_value_head: usize,
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head_dim: usize,
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cache: Cache,
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}
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impl CausalSelfAttention {
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fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
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let (b_sz, seq_len, h, n_embd) = x.dims4()?;
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let cos = self.cache.cos.i(index_pos..index_pos + seq_len)?;
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let sin = self.cache.sin.i(index_pos..index_pos + seq_len)?;
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let cos = cos.unsqueeze(1)?;
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let sin = sin.unsqueeze(1)?;
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let cos = cos.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
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let sin = sin.broadcast_as((b_sz, seq_len, 1, n_embd / 2, 1))?;
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let x = x.reshape((b_sz, seq_len, h, n_embd / 2, 2))?;
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let x0 = x.narrow(D::Minus1, 0, 1)?;
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let x1 = x.narrow(D::Minus1, 1, 1)?;
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let dst0 = (x0.broadcast_mul(&cos)? - x1.broadcast_mul(&sin)?)?;
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let dst1 = (x0.broadcast_mul(&sin)? + x1.broadcast_mul(&cos)?)?;
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let rope = Tensor::cat(&[&dst0, &dst1], D::Minus1)?.reshape((b_sz, seq_len, h, n_embd))?;
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Ok(rope)
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}
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fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
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let (b_sz, seq_len, n_embd) = x.dims3()?;
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let q = self.q_proj.forward(x)?;
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let k = self.k_proj.forward(x)?;
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let v = self.v_proj.forward(x)?;
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let q = q.reshape((b_sz, seq_len, self.n_head, self.head_dim))?;
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let k = k.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
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let mut v = v.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
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let q = self.apply_rotary_emb(&q, index_pos)?;
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let mut k = self.apply_rotary_emb(&k, index_pos)?;
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if self.cache.use_kv_cache {
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let mut cache = self.cache.kvs.lock().unwrap();
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if let Some((cache_k, cache_v)) = &cache[block_idx] {
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k = Tensor::cat(&[cache_k, &k], 1)?.contiguous()?;
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v = Tensor::cat(&[cache_v, &v], 1)?.contiguous()?;
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}
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cache[block_idx] = Some((k.clone(), v.clone()))
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}
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let k = self.repeat_kv(k)?;
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let v = self.repeat_kv(v)?;
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let q = q.transpose(1, 2)?.contiguous()?;
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let k = k.transpose(1, 2)?.contiguous()?;
|
||||
let v = v.transpose(1, 2)?.contiguous()?;
|
||||
|
||||
let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
|
||||
let mask = self.cache.mask(seq_len)?.broadcast_as(att.shape())?;
|
||||
let att = masked_fill(&att, &mask, f32::NEG_INFINITY)?;
|
||||
let att = candle_nn::ops::softmax(&att, D::Minus1)?;
|
||||
// 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.o_proj.forward(&y)?;
|
||||
Ok(y)
|
||||
}
|
||||
|
||||
fn repeat_kv(&self, x: Tensor) -> Result<Tensor> {
|
||||
let n_rep = self.n_head / self.n_key_value_head;
|
||||
if n_rep == 1 {
|
||||
Ok(x)
|
||||
} else {
|
||||
let (b_sz, seq_len, n_kv_head, head_dim) = x.dims4()?;
|
||||
let x = x
|
||||
.unsqueeze(3)?
|
||||
.expand((b_sz, seq_len, n_kv_head, n_rep, head_dim))?
|
||||
.reshape((b_sz, seq_len, n_kv_head * n_rep, head_dim))?;
|
||||
Ok(x)
|
||||
}
|
||||
}
|
||||
|
||||
fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
|
||||
let size_in = cfg.dim;
|
||||
let size_q = (cfg.dim / cfg.n_heads) * cfg.n_heads;
|
||||
let size_kv = (cfg.dim / cfg.n_heads) * cfg.n_kv_heads;
|
||||
let q_proj = linear(size_in, size_q, vb.pp("q_proj"))?;
|
||||
let k_proj = linear(size_in, size_kv, vb.pp("k_proj"))?;
|
||||
let v_proj = linear(size_in, size_kv, vb.pp("v_proj"))?;
|
||||
let o_proj = linear(size_q, size_in, vb.pp("o_proj"))?;
|
||||
Ok(Self {
|
||||
q_proj,
|
||||
k_proj,
|
||||
v_proj,
|
||||
o_proj,
|
||||
n_head: cfg.n_heads,
|
||||
n_key_value_head: cfg.n_kv_heads,
|
||||
head_dim: cfg.dim / cfg.n_heads,
|
||||
cache: cache.clone(),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
|
||||
let shape = mask.shape();
|
||||
let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
|
||||
let m = mask.where_cond(&on_true, on_false)?;
|
||||
Ok(m)
|
||||
}
|
||||
|
||||
struct Mlp {
|
||||
c_fc1: Linear,
|
||||
c_fc2: Linear,
|
||||
c_proj: Linear,
|
||||
}
|
||||
|
||||
impl Mlp {
|
||||
fn new(c_fc1: Linear, c_fc2: Linear, c_proj: Linear) -> Self {
|
||||
Self {
|
||||
c_fc1,
|
||||
c_fc2,
|
||||
c_proj,
|
||||
}
|
||||
}
|
||||
|
||||
fn forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
let x = (silu(&self.c_fc1.forward(x)?)? * self.c_fc2.forward(x)?)?;
|
||||
self.c_proj.forward(&x)
|
||||
}
|
||||
|
||||
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
|
||||
let h_size = cfg.dim;
|
||||
let i_size = cfg.hidden_dim;
|
||||
let c_fc1 = linear(h_size, i_size, vb.pp("gate_proj"))?;
|
||||
let c_fc2 = linear(h_size, i_size, vb.pp("up_proj"))?;
|
||||
let c_proj = linear(i_size, h_size, vb.pp("down_proj"))?;
|
||||
Ok(Self::new(c_fc1, c_fc2, c_proj))
|
||||
}
|
||||
}
|
||||
|
||||
struct Block {
|
||||
rms_1: RmsNorm,
|
||||
attn: CausalSelfAttention,
|
||||
rms_2: RmsNorm,
|
||||
mlp: Mlp,
|
||||
}
|
||||
|
||||
impl Block {
|
||||
fn new(rms_1: RmsNorm, attn: CausalSelfAttention, rms_2: RmsNorm, mlp: Mlp) -> Self {
|
||||
Self {
|
||||
rms_1,
|
||||
attn,
|
||||
rms_2,
|
||||
mlp,
|
||||
}
|
||||
}
|
||||
|
||||
fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
|
||||
let residual = x;
|
||||
let x = self.rms_1.forward(x)?;
|
||||
let x = (self.attn.forward(&x, index_pos, block_idx)? + residual)?;
|
||||
let residual = &x;
|
||||
let x = (self.mlp.forward(&self.rms_2.forward(&x)?)? + residual)?;
|
||||
Ok(x)
|
||||
}
|
||||
|
||||
fn load(vb: VarBuilder, cache: &Cache, cfg: &Config) -> Result<Self> {
|
||||
let attn = CausalSelfAttention::load(vb.pp("self_attn"), cache, cfg)?;
|
||||
let mlp = Mlp::load(vb.pp("mlp"), cfg)?;
|
||||
let input_layernorm = RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("input_layernorm"))?;
|
||||
let post_attention_layernorm =
|
||||
RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("post_attention_layernorm"))?;
|
||||
Ok(Self::new(
|
||||
input_layernorm,
|
||||
attn,
|
||||
post_attention_layernorm,
|
||||
mlp,
|
||||
))
|
||||
}
|
||||
}
|
||||
|
||||
pub struct QLlama {
|
||||
wte: Embedding,
|
||||
blocks: Vec<Block>,
|
||||
ln_f: RmsNorm,
|
||||
lm_head: Linear,
|
||||
pub config: Config,
|
||||
}
|
||||
|
||||
impl QLlama {
|
||||
pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
|
||||
let (_b_sz, _seq_len) = x.dims2()?;
|
||||
let mut x = self.wte.forward(x)?;
|
||||
for (block_idx, block) in self.blocks.iter().enumerate() {
|
||||
x = block.forward(&x, index_pos, block_idx)?;
|
||||
}
|
||||
let x = self.ln_f.forward(&x)?;
|
||||
let logits = self.lm_head.forward(&x)?;
|
||||
logits.to_dtype(DType::F32)
|
||||
}
|
||||
|
||||
pub fn load(vb: VarBuilder, cache: &Cache, cfg: Config) -> Result<Self> {
|
||||
let wte = Embedding::new(cfg.vocab_size, cfg.dim, vb.pp("model.embed_tokens"))?;
|
||||
let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
|
||||
let ln_f = RmsNorm::new(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
|
||||
let blocks: Vec<_> = (0..cfg.n_layers)
|
||||
.map(|i| Block::load(vb.pp(format!("model.layers.{i}")), cache, &cfg).unwrap())
|
||||
.collect();
|
||||
Ok(Self {
|
||||
wte,
|
||||
blocks,
|
||||
ln_f,
|
||||
lm_head,
|
||||
config: cfg,
|
||||
})
|
||||
}
|
||||
}
|
||||
@ -1,168 +0,0 @@
|
||||
use anyhow::Result;
|
||||
use byteorder::{LittleEndian, ReadBytesExt};
|
||||
use candle::{DType, Device, IndexOp, Shape, Tensor};
|
||||
use candle_nn::VarBuilder;
|
||||
|
||||
use crate::model::Config;
|
||||
|
||||
pub struct TransformerWeights {
|
||||
// token embedding table
|
||||
token_embedding_table: Tensor, // (vocab_size, dim)
|
||||
// weights for rmsnorms
|
||||
rms_att_weight: Tensor, // (layer, dim) rmsnorm weights
|
||||
rms_ffn_weight: Tensor, // (layer, dim)
|
||||
// weights for matmuls
|
||||
wq: Tensor, // (layer, dim, dim)
|
||||
wk: Tensor, // (layer, dim, dim)
|
||||
wv: Tensor, // (layer, dim, dim)
|
||||
wo: Tensor, // (layer, dim, dim)
|
||||
// weights for ffn
|
||||
w1: Tensor, // (layer, hidden_dim, dim)
|
||||
w2: Tensor, // (layer, dim, hidden_dim)
|
||||
w3: Tensor, // (layer, hidden_dim, dim)
|
||||
// final rmsnorm
|
||||
rms_final_weight: Tensor, // (dim,)
|
||||
// freq_cis for RoPE relatively positional embeddings
|
||||
freq_cis_real: Tensor, // (seq_len, head_size/2)
|
||||
freq_cis_imag: Tensor, // (seq_len, head_size/2)
|
||||
}
|
||||
|
||||
fn read_i32<R: std::io::Read>(r: &mut R) -> Result<i32> {
|
||||
let mut buf = [0u8; 4];
|
||||
r.read_exact(&mut buf)?;
|
||||
Ok(i32::from_le_bytes(buf))
|
||||
}
|
||||
|
||||
fn read_tensor<R: std::io::Read, S: Into<Shape>>(
|
||||
r: &mut R,
|
||||
shape: S,
|
||||
dev: &Device,
|
||||
) -> Result<Tensor> {
|
||||
let shape = shape.into();
|
||||
let mut data_t = vec![0f32; shape.elem_count()];
|
||||
r.read_f32_into::<LittleEndian>(&mut data_t)?;
|
||||
let tensor = Tensor::from_vec(data_t, shape, dev)?;
|
||||
Ok(tensor)
|
||||
}
|
||||
|
||||
impl Config {
|
||||
pub fn from_reader<R: std::io::Read>(r: &mut R) -> Result<Self> {
|
||||
let dim = read_i32(r)? as usize;
|
||||
let hidden_dim = read_i32(r)? as usize;
|
||||
let n_layers = read_i32(r)? as usize;
|
||||
let n_heads = read_i32(r)? as usize;
|
||||
let n_kv_heads = read_i32(r)? as usize;
|
||||
let vocab_size = read_i32(r)? as usize;
|
||||
let seq_len = read_i32(r)? as usize;
|
||||
Ok(Self {
|
||||
dim,
|
||||
hidden_dim,
|
||||
n_layers,
|
||||
n_heads,
|
||||
n_kv_heads,
|
||||
vocab_size,
|
||||
seq_len,
|
||||
norm_eps: 1e-5,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn head_size(&self) -> usize {
|
||||
self.dim / self.n_heads
|
||||
}
|
||||
}
|
||||
|
||||
impl TransformerWeights {
|
||||
pub fn from_reader<R: std::io::Read>(r: &mut R, c: &Config, dev: &Device) -> Result<Self> {
|
||||
let token_embedding_table = read_tensor(r, (c.vocab_size, c.dim), dev)?;
|
||||
let rms_att_weight = read_tensor(r, (c.n_layers, c.dim), dev)?;
|
||||
let wq = read_tensor(r, (c.n_layers, c.dim, c.dim), dev)?;
|
||||
let wk = read_tensor(r, (c.n_layers, c.dim, c.dim), dev)?;
|
||||
let wv = read_tensor(r, (c.n_layers, c.dim, c.dim), dev)?;
|
||||
let wo = read_tensor(r, (c.n_layers, c.dim, c.dim), dev)?;
|
||||
let rms_ffn_weight = read_tensor(r, (c.n_layers, c.dim), dev)?;
|
||||
let w1 = read_tensor(r, (c.n_layers, c.hidden_dim, c.dim), dev)?;
|
||||
let w2 = read_tensor(r, (c.n_layers, c.dim, c.hidden_dim), dev)?;
|
||||
let w3 = read_tensor(r, (c.n_layers, c.hidden_dim, c.dim), dev)?;
|
||||
let rms_final_weight = read_tensor(r, c.dim, dev)?;
|
||||
let head_size = c.head_size();
|
||||
let freq_cis_real = read_tensor(r, (c.seq_len, head_size / 2), dev)?;
|
||||
let freq_cis_imag = read_tensor(r, (c.seq_len, head_size / 2), dev)?;
|
||||
Ok(Self {
|
||||
token_embedding_table,
|
||||
rms_att_weight,
|
||||
wq,
|
||||
wk,
|
||||
wv,
|
||||
wo,
|
||||
rms_ffn_weight,
|
||||
w1,
|
||||
w2,
|
||||
w3,
|
||||
rms_final_weight,
|
||||
freq_cis_real,
|
||||
freq_cis_imag,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn var_builder(&self, cfg: &Config, device: &Device) -> Result<VarBuilder<'static>> {
|
||||
// TODO: As of 2023-08-04, gemm is slower than expected when multiplying a matrix of
|
||||
// size (1, k) with the transpose of a matrix of size (k, n) as it ends up transposing the
|
||||
// second matrix back. We detect this case here and as a temporary hack make the weight
|
||||
// matrix column major rather than row major. This ends up speeding up text generation from
|
||||
// 120 token/s to 220 token/s on a Ryzen 2600X.
|
||||
let tr = device.is_cpu() && !candle::utils::has_mkl();
|
||||
let tr = |x: Tensor| if tr { x.t()?.contiguous()?.t() } else { Ok(x) };
|
||||
let mut ws = std::collections::HashMap::new();
|
||||
let mut insert = |name: &str, t: Tensor| {
|
||||
ws.insert(name.to_string(), t);
|
||||
};
|
||||
insert("rot.freq_cis_real", self.freq_cis_real.clone());
|
||||
insert("rot.freq_cis_imag", self.freq_cis_imag.clone());
|
||||
insert(
|
||||
"model.embed_tokens.weight",
|
||||
self.token_embedding_table.clone(),
|
||||
);
|
||||
insert("lm_head.weight", tr(self.token_embedding_table.clone())?);
|
||||
insert("model.norm.weight", self.rms_final_weight.clone());
|
||||
for layer in 0..cfg.n_layers {
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.self_attn.q_proj.weight"),
|
||||
tr(self.wq.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.self_attn.k_proj.weight"),
|
||||
tr(self.wk.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.self_attn.v_proj.weight"),
|
||||
tr(self.wv.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.self_attn.o_proj.weight"),
|
||||
tr(self.wo.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.mlp.gate_proj.weight"),
|
||||
tr(self.w1.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.mlp.down_proj.weight"),
|
||||
tr(self.w2.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.mlp.up_proj.weight"),
|
||||
tr(self.w3.i(layer)?)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.input_layernorm.weight"),
|
||||
self.rms_att_weight.i(layer)?,
|
||||
);
|
||||
ws.insert(
|
||||
format!("model.layers.{layer}.post_attention_layernorm.weight"),
|
||||
self.rms_ffn_weight.i(layer)?,
|
||||
);
|
||||
}
|
||||
let vb = VarBuilder::from_tensors(ws, DType::F32, device);
|
||||
Ok(vb)
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user