use crate::models::with_tracing::{layer_norm, linear, Embedding, LayerNorm, Linear};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{Activation, VarBuilder};
use serde::Deserialize;
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {
pub(crate) vocab_size: usize,
pub(crate) hidden_size: usize,
pub(crate) intermediate_size: usize,
pub(crate) num_hidden_layers: usize,
pub(crate) num_attention_heads: usize,
pub(crate) num_key_value_heads: Option<usize>,
pub(crate) hidden_act: Activation,
pub(crate) max_position_embeddings: usize,
pub(crate) layer_norm_eps: f64,
pub(crate) tie_word_embeddings: bool,
pub(crate) rope_theta: f32,
pub(crate) partial_rotary_factor: f64,
pub(crate) qk_layernorm: bool,
}
impl Config {
fn num_key_value_heads(&self) -> usize {
self.num_key_value_heads.unwrap_or(self.num_attention_heads)
}
fn head_dim(&self) -> usize {
self.hidden_size / self.num_attention_heads
}
}
#[derive(Debug, Clone)]
struct RotaryEmbedding {
dim: usize,
sin: Tensor,
cos: Tensor,
}
impl RotaryEmbedding {
fn new(cfg: &Config, dev: &Device) -> Result<Self> {
let dim = (cfg.partial_rotary_factor * cfg.head_dim() as f64) as usize;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / cfg.rope_theta.powf(i as f32 / dim as f32))
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
let t = Tensor::arange(0u32, cfg.max_position_embeddings as u32, dev)?
.to_dtype(DType::F32)?
.reshape((cfg.max_position_embeddings, 1))?;
let freqs = t.matmul(&inv_freq)?;
let emb = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
Ok(Self {
dim,
sin: emb.sin()?,
cos: emb.cos()?,
})
}
fn apply_rotary_emb(&self, xs: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
let (_b_size, _num_heads, seq_len, _headdim) = xs.dims4()?;
let xs_rot = xs.i((.., .., .., ..self.dim))?;
let xs_pass = xs.i((.., .., .., self.dim..))?;
let xs12 = xs_rot.chunk(2, D::Minus1)?;
let (xs1, xs2) = (&xs12[0], &xs12[1]);
let c = self.cos.narrow(0, seqlen_offset, seq_len)?;
let s = self.sin.narrow(0, seqlen_offset, seq_len)?;
let rotate_half = Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)?;
let xs_rot = (xs_rot.broadcast_mul(&c)? + rotate_half.broadcast_mul(&s)?)?;
Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
}
}
#[derive(Debug, Clone)]
#[allow(clippy::upper_case_acronyms)]
struct MLP {
fc1: Linear,
fc2: Linear,
act: Activation,
}
impl MLP {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let fc1 = linear(cfg.hidden_size, cfg.intermediate_size, vb.pp("fc1"))?;
let fc2 = linear(cfg.intermediate_size, cfg.hidden_size, vb.pp("fc2"))?;
Ok(Self {
fc1,
fc2,
act: cfg.hidden_act,
})
}
}
impl Module for MLP {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.fc1)?.apply(&self.act)?.apply(&self.fc2)
}
}
#[derive(Clone)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
dense: Linear,
kv_cache: Option<(Tensor, Tensor)>,
q_layernorm: Option<LayerNorm>,
k_layernorm: Option<LayerNorm>,
rotary_emb: RotaryEmbedding,
softmax_scale: f64,
num_heads: usize,
num_kv_heads: usize,
head_dim: usize,
span: tracing::Span,
}
fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
let mask: Vec<_> = (0..size)
.flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
.collect();
Tensor::from_slice(&mask, (size, size), device)
}
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)
}
impl Attention {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads();
let head_dim = cfg.head_dim();
let q_proj = linear(cfg.hidden_size, num_heads * head_dim, vb.pp("q_proj"))?;
let k_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("k_proj"))?;
let v_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("v_proj"))?;
let dense = linear(num_heads * head_dim, cfg.hidden_size, vb.pp("dense"))?;
let rotary_emb = RotaryEmbedding::new(cfg, vb.device())?;
let (q_layernorm, k_layernorm) = if cfg.qk_layernorm {
let q_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("q_layernorm"))?;
let k_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("k_layernorm"))?;
(Some(q_layernorm), Some(k_layernorm))
} else {
(None, None)
};
let softmax_scale = 1f64 / (head_dim as f64).sqrt();
Ok(Self {
q_proj,
k_proj,
v_proj,
dense,
kv_cache: None,
q_layernorm,
k_layernorm,
rotary_emb,
softmax_scale,
num_heads,
num_kv_heads,
head_dim,
span: tracing::span!(tracing::Level::TRACE, "attention"),
})
}
fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
crate::utils::repeat_kv(xs, self.num_heads / self.num_kv_heads)
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let (b_size, seq_len, _n_embd) = 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 = match &self.q_layernorm {
None => query_states,
Some(ln) => query_states.apply(ln)?,
};
let key_states = match &self.k_layernorm {
None => key_states,
Some(ln) => key_states.apply(ln)?,
};
let query_states = query_states
.reshape((b_size, seq_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?;
let key_states = key_states
.reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let value_states = value_states
.reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let seqlen_offset = match &self.kv_cache {
None => 0,
Some((prev_k, _)) => prev_k.dim(2)?,
};
let query_states = self
.rotary_emb
.apply_rotary_emb(&query_states, seqlen_offset)?;
let key_states = self
.rotary_emb
.apply_rotary_emb(&key_states, seqlen_offset)?;
let (key_states, value_states) = match &self.kv_cache {
None => (key_states, value_states),
Some((prev_k, prev_v)) => {
let k = Tensor::cat(&[prev_k, &key_states], 2)?;
let v = Tensor::cat(&[prev_v, &value_states], 2)?;
(k, v)
}
};
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_weights = (query_states
.to_dtype(DType::F32)?
.contiguous()?
.matmul(&key_states.to_dtype(DType::F32)?.t()?)?
* self.softmax_scale)?;
let attn_weights = match mask {
None => attn_weights,
Some(mask) => masked_fill(
&attn_weights,
&mask.broadcast_left((b_size, self.num_heads))?,
f32::NEG_INFINITY,
)?,
};
let attn_weights =
candle_nn::ops::softmax_last_dim(&attn_weights)?.to_dtype(value_states.dtype())?;
let attn_output = attn_weights.matmul(&value_states)?;
let attn_output = attn_output
.transpose(1, 2)?
.reshape((b_size, seq_len, ()))?;
attn_output.apply(&self.dense)
}
fn clear_kv_cache(&mut self) {
self.kv_cache = None
}
}
#[derive(Clone)]
struct DecoderLayer {
self_attn: Attention,
mlp: MLP,
input_layernorm: LayerNorm,
span: tracing::Span,
}
impl DecoderLayer {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let self_attn = Attention::new(cfg, vb.pp("self_attn"))?;
let mlp = MLP::new(cfg, vb.pp("mlp"))?;
let input_layernorm = layer_norm(
cfg.hidden_size,
cfg.layer_norm_eps,
vb.pp("input_layernorm"),
)?;
Ok(Self {
self_attn,
mlp,
input_layernorm,
span: tracing::span!(tracing::Level::TRACE, "block"),
})
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let residual = xs;
let xs = xs.apply(&self.input_layernorm)?;
let attn_outputs = self.self_attn.forward(&xs, mask)?;
let feed_forward_hidden_states = self.mlp.forward(&xs)?;
attn_outputs + feed_forward_hidden_states + residual
}
fn clear_kv_cache(&mut self) {
self.self_attn.clear_kv_cache()
}
}
#[derive(Clone)]
pub struct Model {
embed_tokens: Embedding,
layers: Vec<DecoderLayer>,
final_layernorm: LayerNorm,
lm_head: Linear,
span: tracing::Span,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_m = vb.pp("model");
let embed_tokens =
Embedding::new(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
let final_layernorm = layer_norm(
cfg.hidden_size,
cfg.layer_norm_eps,
vb_m.pp("final_layernorm"),
)?;
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_m = vb_m.pp("layers");
for layer_idx in 0..cfg.num_hidden_layers {
let layer = DecoderLayer::new(cfg, vb_m.pp(layer_idx))?;
layers.push(layer)
}
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
Ok(Self {
embed_tokens,
layers,
final_layernorm,
lm_head,
span: tracing::span!(tracing::Level::TRACE, "model"),
})
}
pub fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (_b_size, seq_len) = xs.dims2()?;
let mut xs = xs.apply(&self.embed_tokens)?;
let mask = if seq_len <= 1 {
None
} else {
Some(get_mask(seq_len, xs.device())?)
};
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, mask.as_ref())?;
}
xs.apply(&self.final_layernorm)?
.narrow(1, seq_len - 1, 1)?
.apply(&self.lm_head)?
.squeeze(1)
}
pub fn clear_kv_cache(&mut self) {
self.layers.iter_mut().for_each(|b| b.clear_kv_cache())
}
}