use std::collections::HashMap;
use candle::quantized::gguf_file;
use candle::quantized::QTensor;
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{Embedding, LayerNorm};
pub const MAX_SEQ_LEN: usize = 4096;
#[derive(Debug, Clone)]
struct QLinear {
inner: candle::quantized::QMatMul,
bias: Tensor,
span: tracing::Span,
}
impl QLinear {
fn new<R: std::io::Read + std::io::Seek>(
ct: &gguf_file::Content,
r: &mut R,
name: &str,
device: &Device,
) -> Result<Self> {
let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
let w = ct.tensor(r, &format!("{name}.weight"), device)?;
let b = ct.tensor(r, &format!("{name}.bias"), device)?;
let inner = candle::quantized::QMatMul::from_qtensor(w)?;
let bias = b.dequantize(device)?;
Ok(Self { inner, bias, span })
}
}
impl Module for QLinear {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
self.inner.forward(xs)?.broadcast_add(&self.bias)
}
}
#[derive(Debug, Clone)]
struct Mlp {
ffn_up: QLinear,
ffn_down: QLinear,
}
impl Module for Mlp {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.ffn_up)?.gelu()?.apply(&self.ffn_down)
}
}
#[derive(Debug, Clone)]
struct LayerWeights {
attn_qkv: QLinear,
attn_output: QLinear,
attn_norm: LayerNorm,
mlp: Mlp,
n_head: usize,
n_kv_head: usize,
head_dim: usize,
cos: Tensor,
sin: Tensor,
rope_dim: usize,
neg_inf: Tensor,
kv_cache: Option<(Tensor, Tensor)>,
span_attn: tracing::Span,
span_rot: tracing::Span,
}
fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: &Tensor) -> Result<Tensor> {
let shape = mask.shape();
let m = mask.where_cond(&on_true.broadcast_as(shape.dims())?, on_false)?;
Ok(m)
}
impl LayerWeights {
fn apply_rotary_emb(&self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
let _enter = self.span_rot.enter();
let (_b_sz, _n_head, seq_len, _n_embd) = xs.dims4()?;
let xs_rot = xs.i((.., .., .., ..self.rope_dim))?;
let xs_pass = xs.i((.., .., .., self.rope_dim..))?;
let cos = self.cos.narrow(0, index_pos, seq_len)?;
let sin = self.sin.narrow(0, index_pos, seq_len)?;
let xs_rot = candle_nn::rotary_emb::rope(&xs_rot.contiguous()?, &cos, &sin)?;
Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
}
fn forward_attn(
&mut self,
x: &Tensor,
mask: Option<&Tensor>,
index_pos: usize,
) -> Result<Tensor> {
let _enter = self.span_attn.enter();
let (b_sz, seq_len, n_embd) = x.dims3()?;
let qkv =
self.attn_qkv
.forward(x)?
.reshape((b_sz, seq_len, 3, self.n_head, self.head_dim))?;
let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
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)?;
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),
};
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)
}
}