use crate::models::with_tracing::{linear_no_bias, Linear, RmsNorm};
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{Activation, VarBuilder};
use serde::Deserialize;
use std::sync::Arc;
#[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: usize,
pub(crate) hidden_act: Activation,
pub(crate) max_position_embeddings: usize,
pub(crate) rms_norm_eps: f64,
pub(crate) rope_theta: f64,
pub(crate) sliding_window: usize,
pub(crate) num_experts_per_tok: usize,
pub(crate) num_local_experts: usize,
pub(crate) use_flash_attn: bool,
}
impl Config {
pub fn v0_1_8x7b(use_flash_attn: bool) -> Self {
Self {
vocab_size: 32000,
hidden_size: 4096,
intermediate_size: 14336,
num_hidden_layers: 32,
num_attention_heads: 32,
num_key_value_heads: 8,
hidden_act: Activation::Silu,
max_position_embeddings: 32768,
rms_norm_eps: 1e-5,
rope_theta: 1e6,
sliding_window: 4096,
num_experts_per_tok: 2,
num_local_experts: 8,
use_flash_attn,
}
}
}
#[derive(Debug, Clone)]
struct RotaryEmbedding {
sin: Tensor,
cos: Tensor,
}
fn rotate_half(xs: &Tensor) -> Result<Tensor> {
let last_dim = xs.dim(D::Minus1)?;
let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
}
impl RotaryEmbedding {
fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
let dim = cfg.hidden_size / cfg.num_attention_heads;
let max_seq_len = cfg.max_position_embeddings;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / (cfg.rope_theta as f32).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)?.to_dtype(dtype)?;
let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((max_seq_len, 1))?;
let freqs = t.matmul(&inv_freq)?;
let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
Ok(Self {
sin: freqs.sin()?,
cos: freqs.cos()?,
})
}
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 cos = cos.unsqueeze(0)?.unsqueeze(0)?; let sin = sin.unsqueeze(0)?.unsqueeze(0)?; let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
Ok((q_embed, k_embed))
}
}
#[cfg(feature = "flash-attn")]
fn flash_attn(
q: &Tensor,
k: &Tensor,
v: &Tensor,
softmax_scale: f32,
causal: bool,
) -> Result<Tensor> {
candle_flash_attn::flash_attn(q, k, v, softmax_scale, causal)
}
#[cfg(not(feature = "flash-attn"))]
fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Tensor> {
unimplemented!("compile with '--features flash-attn'")
}
#[derive(Debug, Clone)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
o_proj: Linear,
num_heads: usize,
num_kv_heads: usize,
num_kv_groups: usize,
head_dim: usize,
hidden_size: usize,
rotary_emb: Arc<RotaryEmbedding>,
kv_cache: Option<(Tensor, Tensor)>,
use_flash_attn: bool,
}
impl Attention {
fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_sz = cfg.hidden_size;
let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads;
let num_kv_groups = num_heads / num_kv_heads;
let head_dim = hidden_sz / num_heads;
let q_proj = linear_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
let v_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
Ok(Self {
q_proj,
k_proj,
v_proj,
o_proj,
num_heads,
num_kv_heads,
num_kv_groups,
head_dim,
hidden_size: hidden_sz,
rotary_emb,
kv_cache: None,
use_flash_attn: cfg.use_flash_attn,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Tensor> {
let (b_sz, q_len, _) = 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 = 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)?;
let value_states = crate::utils::repeat_kv(value_states, self.num_kv_groups)?;
let attn_output = if self.use_flash_attn {
let q = query_states.transpose(1, 2)?;
let k = key_states.transpose(1, 2)?;
let v = value_states.transpose(1, 2)?;
let softmax_scale = 1f32 / (self.head_dim as f32).sqrt();
flash_attn(&q, &k, &v, softmax_scale, q_len > 1)?.transpose(1, 2)?
} else {
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, self.hidden_size))?
.apply(&self.o_proj)
}
}
#[derive(Debug, Clone)]
struct BlockSparseTop2MLP {
w1: Linear,
w2: Linear,
w3: Linear,
act_fn: Activation,
}
impl BlockSparseTop2MLP {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_sz = cfg.hidden_size;
let intermediate_sz = cfg.intermediate_size;
let w1 = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("w1"))?;
let w2 = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("w2"))?;
let w3 = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("w3"))?;
Ok(Self {
w1,
w2,
w3,
act_fn: cfg.hidden_act,
})
}
}
impl Module for BlockSparseTop2MLP {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let lhs = xs.apply(&self.w1)?.apply(&self.act_fn)?;
let rhs = xs.apply(&self.w3)?;
(lhs * rhs)?.apply(&self.w2)
}
}
#[derive(Debug, Clone)]
struct SparseMoeBlock {
gate: Linear,
experts: Vec<BlockSparseTop2MLP>,
num_experts_per_tok: usize,
}
impl SparseMoeBlock {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let gate = linear_no_bias(cfg.hidden_size, cfg.num_local_experts, vb.pp("gate"))?;
let mut experts = Vec::with_capacity(cfg.num_local_experts);
let vb = vb.pp("experts");
for idx in 0..cfg.num_local_experts {
let expert = BlockSparseTop2MLP::new(cfg, vb.pp(idx))?;
experts.push(expert)
}
Ok(SparseMoeBlock {
gate,
experts,
num_experts_per_tok: cfg.num_experts_per_tok,
})
}
}
impl Module for SparseMoeBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let (b_size, seq_len, hidden_dim) = xs.dims3()?;
let xs = xs.reshape(((), hidden_dim))?;
let router_logits = xs.apply(&self.gate)?;
let routing_weights = candle_nn::ops::softmax_last_dim(&router_logits)?;
let routing_weights = routing_weights.to_dtype(DType::F32)?.to_vec2::<f32>()?;
let mut top_x = vec![vec![]; self.experts.len()];
let mut selected_rws = vec![vec![]; self.experts.len()];
for (row_idx, rw) in routing_weights.iter().enumerate() {
let mut dst = (0..rw.len() as u32).collect::<Vec<u32>>();
dst.sort_by(|&i, &j| rw[j as usize].total_cmp(&rw[i as usize]));
let mut sum_routing_weights = 0f32;
for &expert_idx in dst.iter().take(self.num_experts_per_tok) {
let expert_idx = expert_idx as usize;
let routing_weight = rw[expert_idx];
sum_routing_weights += routing_weight;
top_x[expert_idx].push(row_idx as u32);
}
for &expert_idx in dst.iter().take(self.num_experts_per_tok) {
let expert_idx = expert_idx as usize;
let routing_weight = rw[expert_idx];
selected_rws[expert_idx].push(routing_weight / sum_routing_weights)
}
}
let mut ys = xs.zeros_like()?;
for (expert_idx, expert_layer) in self.experts.iter().enumerate() {
let top_x = &top_x[expert_idx];
if top_x.is_empty() {
continue;
}
let top_x = Tensor::new(top_x.as_slice(), xs.device())?;
let selected_rws =
Tensor::new(selected_rws[expert_idx].as_slice(), xs.device())?.reshape(((), 1))?;
let current_state = xs.index_select(&top_x, 0)?.reshape(((), hidden_dim))?;
let current_hidden_states = expert_layer.forward(¤t_state)?;
let current_hidden_states = current_hidden_states.broadcast_mul(&selected_rws)?;
ys = ys.index_add(&top_x, ¤t_hidden_states, 0)?;
}
let ys = ys.reshape((b_size, seq_len, hidden_dim))?;
Ok(ys)
}
}
#[derive(Debug, Clone)]
struct DecoderLayer {
self_attn: Attention,
block_sparse_moe: SparseMoeBlock,
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 block_sparse_moe = SparseMoeBlock::new(cfg, vb.pp("block_sparse_moe"))?;
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,
block_sparse_moe,
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.block_sparse_moe)?;
residual + xs
}
}
#[derive(Debug, Clone)]
pub struct Model {
embed_tokens: candle_nn::Embedding,
layers: Vec<DecoderLayer>,
norm: RmsNorm,
lm_head: Linear,
sliding_window: usize,
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 = linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
Ok(Self {
embed_tokens,
layers,
norm,
lm_head,
sliding_window: cfg.sliding_window,
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 || j + self.sliding_window < i {
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)
}
}