use crate::models::with_tracing::{linear_no_bias as linear, Linear, RmsNorm};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
use std::sync::Arc;
#[derive(Debug, Clone, serde::Deserialize)]
pub enum RopeScalingType {
#[serde(rename = "longrope")]
LongRope,
}
#[derive(Debug, Clone, serde::Deserialize)]
pub struct RopeScaling {
pub short_factor: Vec<f32>,
pub long_factor: Vec<f32>,
#[serde(rename = "type")]
pub type_: RopeScalingType,
}
#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
pub vocab_size: usize,
pub hidden_act: candle_nn::Activation,
pub hidden_size: usize,
pub intermediate_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub num_key_value_heads: usize,
pub rms_norm_eps: f64,
pub rope_theta: f64,
pub bos_token_id: Option<u32>,
pub eos_token_id: Option<u32>,
pub rope_scaling: Option<RopeScaling>,
pub max_position_embeddings: usize,
pub original_max_position_embeddings: Option<usize>,
pub partial_rotary_factor: Option<f64>,
#[serde(default)]
pub tie_word_embeddings: bool,
}
impl Config {
pub fn head_dim(&self) -> usize {
self.hidden_size / self.num_attention_heads
}
}
#[derive(Debug, Clone)]
pub struct RotaryEmbedding {
partial_dim: Option<usize>,
sin: Tensor,
cos: Tensor,
}
impl RotaryEmbedding {
pub fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
let partial_dim = cfg
.partial_rotary_factor
.as_ref()
.map(|v| (v * cfg.head_dim() as f64) as usize);
let dim = partial_dim.unwrap_or(cfg.head_dim());
let freqs = match cfg.rope_scaling.as_ref() {
None => {
let max_seq_len = cfg.max_position_embeddings;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
.collect();
let inv_freq = Tensor::from_vec(inv_freq, (1, ()), dev)?.to_dtype(dtype)?;
let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((max_seq_len, 1))?;
t.matmul(&inv_freq)?
}
Some(rope_scaling) => {
let inv_freq_s: Vec<_> = (0..dim)
.step_by(2)
.zip(rope_scaling.short_factor.iter())
.map(|(i, &f)| f / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
.collect();
let inv_freq_s = Tensor::from_vec(inv_freq_s, (1, ()), dev)?.to_dtype(dtype)?;
let max_seq_len = cfg.max_position_embeddings;
match cfg.original_max_position_embeddings {
None => {
let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((max_seq_len, 1))?;
t.matmul(&inv_freq_s)?
}
Some(original_max_seq_len) => {
let t_s = Tensor::arange(0u32, original_max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((original_max_seq_len, 1))?;
let freq_s = t_s.matmul(&inv_freq_s)?;
let inv_freq_l: Vec<_> = (0..dim)
.step_by(2)
.zip(rope_scaling.long_factor.iter())
.map(|(i, &f)| f / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
.collect();
let inv_freq_l =
Tensor::from_vec(inv_freq_l, (1, ()), dev)?.to_dtype(dtype)?;
let t_l =
Tensor::arange(original_max_seq_len as u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape(((), 1))?;
let freq_l = t_l.matmul(&inv_freq_l)?;
Tensor::cat(&[&freq_s, &freq_l], 0)?
}
}
}
};
Ok(Self {
partial_dim,
sin: freqs.sin()?,
cos: freqs.cos()?,
})
}
fn rope(&self, xs: &Tensor, cos: &Tensor, sin: &Tensor) -> Result<Tensor> {
let x = match self.partial_dim {
None => candle_nn::rotary_emb::rope(&xs.contiguous()?, cos, sin)?,
Some(dim) => {
let xs_rot = xs.i((.., .., .., ..dim))?.contiguous()?;
let xs_pass = xs.i((.., .., .., dim..))?;
let xs_rot = candle_nn::rotary_emb::rope(&xs_rot, cos, sin)?;
Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)?.contiguous()?
}
};
Ok(x)
}
pub fn apply_rotary_emb_qkv(
&self,
q: &Tensor,
k: &Tensor,
seqlen_offset: usize,
) -> Result<(Tensor, Tensor)> {
let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
let q_embed = self.rope(&q.contiguous()?, &cos, &sin)?;
let k_embed = self.rope(&k.contiguous()?, &cos, &sin)?;
Ok((q_embed, k_embed))
}
}
#[derive(Debug, Clone)]
struct Attention {
qkv_proj: Linear,
o_proj: Linear,
num_heads: usize,
num_kv_heads: usize,
num_kv_groups: usize,
head_dim: usize,
rotary_emb: Arc<RotaryEmbedding>,
kv_cache: Option<(Tensor, Tensor)>,
}
impl Attention {
fn new(rotary_emb: Arc<RotaryEmbedding>, 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 op_size = num_heads * head_dim + 2 * num_kv_heads * head_dim;
let qkv_proj = linear(cfg.hidden_size, op_size, vb.pp("qkv_proj"))?;
let o_proj = linear(num_heads * head_dim, cfg.hidden_size, vb.pp("o_proj"))?;
Ok(Self {
qkv_proj,
o_proj,
rotary_emb,
kv_cache: None,
num_heads,
num_kv_heads,
num_kv_groups: num_heads / num_kv_heads,
head_dim,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Tensor> {
let (b_sz, q_len, _) = xs.dims3()?;
let qkv = self.qkv_proj.forward(xs)?;
let query_pos = self.num_heads * self.head_dim;
let query_states = qkv.narrow(D::Minus1, 0, query_pos)?;
let key_states = qkv.narrow(D::Minus1, query_pos, self.num_kv_heads * self.head_dim)?;
let value_states = qkv.narrow(
D::Minus1,
query_pos + self.num_kv_heads * self.head_dim,
self.num_kv_heads * self.head_dim,
)?;
let query_states = query_states
.reshape((b_sz, q_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?;
let key_states = key_states
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let value_states = value_states
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let (query_states, key_states) =
self.rotary_emb
.apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
let (key_states, value_states) = match &self.kv_cache {
None => (key_states, value_states),
Some((prev_k, prev_v)) => {
let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
(key_states, value_states)
}
};
self.kv_cache = Some((key_states.clone(), value_states.clone()));
let key_states = crate::utils::repeat_kv(key_states, self.num_kv_groups)?.contiguous()?;
let value_states =
crate::utils::repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
let attn_output = {
let scale = 1f64 / f64::sqrt(self.head_dim as f64);
let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
let attn_weights = match attention_mask {
None => attn_weights,
Some(mask) => attn_weights.broadcast_add(mask)?,
};
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
attn_weights.matmul(&value_states)?
};
attn_output
.transpose(1, 2)?
.reshape((b_sz, q_len, ()))?
.apply(&self.o_proj)
}
fn clear_kv_cache(&mut self) {
self.kv_cache = None
}
}
#[derive(Debug, Clone)]
struct Mlp {
gate_up_proj: Linear,
down_proj: Linear,
act_fn: candle_nn::Activation,
i_size: usize,
}
impl Mlp {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let i_size = cfg.intermediate_size;
let gate_up_proj = linear(hidden_size, 2 * i_size, vb.pp("gate_up_proj"))?;
let down_proj = linear(i_size, hidden_size, vb.pp("down_proj"))?;
Ok(Self {
gate_up_proj,
down_proj,
act_fn: cfg.hidden_act,
i_size,
})
}
}
impl Module for Mlp {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let up_states = xs.apply(&self.gate_up_proj)?;
let gate = up_states.narrow(D::Minus1, 0, self.i_size)?;
let up_states = up_states.narrow(D::Minus1, self.i_size, self.i_size)?;
let up_states = (up_states * gate.apply(&self.act_fn))?;
up_states.apply(&self.down_proj)
}
}
#[derive(Debug, Clone)]
struct DecoderLayer {
self_attn: Attention,
mlp: Mlp,
input_layernorm: RmsNorm,
post_attention_layernorm: RmsNorm,
}
impl DecoderLayer {
fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
let mlp = Mlp::new(cfg, vb.pp("mlp"))?;
let input_layernorm =
RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
let post_attention_layernorm = RmsNorm::new(
cfg.hidden_size,
cfg.rms_norm_eps,
vb.pp("post_attention_layernorm"),
)?;
Ok(Self {
self_attn,
mlp,
input_layernorm,
post_attention_layernorm,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Tensor> {
let residual = xs;
let xs = self.input_layernorm.forward(xs)?;
let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
let xs = (xs + residual)?;
let residual = &xs;
let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
residual + xs
}
fn clear_kv_cache(&mut self) {
self.self_attn.clear_kv_cache()
}
}
#[derive(Debug, Clone)]
pub struct Model {
embed_tokens: candle_nn::Embedding,
layers: Vec<DecoderLayer>,
norm: RmsNorm,
lm_head: Linear,
device: Device,
dtype: DType,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_m = vb.pp("model");
let embed_tokens =
candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_l = vb_m.pp("layers");
for layer_idx in 0..cfg.num_hidden_layers {
let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
layers.push(layer)
}
let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
let lm_head = if cfg.tie_word_embeddings {
Linear::from_weights(embed_tokens.embeddings().clone(), None)
} else {
linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?
};
Ok(Self {
embed_tokens,
layers,
norm,
lm_head,
device: vb.device().clone(),
dtype: vb.dtype(),
})
}
fn prepare_decoder_attention_mask(
&self,
b_size: usize,
tgt_len: usize,
seqlen_offset: usize,
) -> Result<Tensor> {
let mask: Vec<_> = (0..tgt_len)
.flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0. }))
.collect();
let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
let mask = if seqlen_offset > 0 {
let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
Tensor::cat(&[&mask0, &mask], D::Minus1)?
} else {
mask
};
mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
.to_dtype(self.dtype)
}
pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
let (b_size, seq_len) = input_ids.dims2()?;
let attention_mask = if seq_len <= 1 {
None
} else {
let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
Some(mask)
};
let mut xs = self.embed_tokens.forward(input_ids)?;
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
}
xs.narrow(1, seq_len - 1, 1)?
.apply(&self.norm)?
.apply(&self.lm_head)
}
pub fn clear_kv_cache(&mut self) {
for layer in self.layers.iter_mut() {
layer.clear_kv_cache()
}
}
}