candle_transformers/models/

stable_lm.rs

//! StableLM model implementation.
//!
//! StableLM is a family of language models trained by Stability AI.
//! This implementation supports the StableLM architecture.
//!
//! Key characteristics:
//! - Grouped query attention (GQA)
//! - Layer normalization
//! - Rotary positional embeddings (RoPE)
//! - Support for different model sizes (3B, 7B)
//!
//! References:
//! - 🤗 [Model Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
//!

use crate::models::with_tracing::{linear, linear_no_bias, Linear};
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{Activation, LayerNorm, VarBuilder};
use serde::Deserialize;
use std::sync::Arc;

// https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/configuration_stablelm.py
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {
    pub(crate) vocab_size: usize,
    pub(crate) intermediate_size: usize,
    pub(crate) hidden_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) partial_rotary_factor: f64,
    pub(crate) rope_theta: f64,
    pub(crate) max_position_embeddings: usize,
    pub(crate) layer_norm_eps: f64,
    pub(crate) use_cache: bool,
    #[serde(default)]
    pub(crate) use_qkv_bias: bool, // Used in StableLM-2
    #[serde(default)]
    pub(crate) use_flash_attn: bool, // Not in config.json
}

impl Config {
    pub fn stablelm_3b_4e1t(use_flash_attn: bool) -> Self {
        Self {
            vocab_size: 50304,
            intermediate_size: 6912,
            hidden_size: 2560,
            num_hidden_layers: 32,
            num_attention_heads: 32,
            num_key_value_heads: 32,
            hidden_act: Activation::Silu,
            partial_rotary_factor: 0.25,
            rope_theta: 10_000.,
            max_position_embeddings: 4096,
            layer_norm_eps: 1e-5,
            use_qkv_bias: false,
            use_cache: true,
            use_flash_attn,
        }
    }

    pub fn head_dim(&self) -> usize {
        self.hidden_size / self.num_attention_heads
    }

    pub fn rotary_ndims(&self) -> usize {
        (self.head_dim() as f64 * self.partial_rotary_factor) as usize
    }

    pub fn num_kv_groups(&self) -> usize {
        self.num_attention_heads / self.num_key_value_heads
    }

    pub fn set_use_flash_attn(&mut self, use_flash_attn: bool) {
        self.use_flash_attn = use_flash_attn
    }
}

#[derive(Debug)]
pub(crate) struct RotaryEmbedding {
    sin: Tensor,
    cos: Tensor,
}

fn rotate_half(xs: &Tensor) -> Result<Tensor> {
    let xs = xs.chunk(2, D::Minus1)?;
    Tensor::cat(&[&xs[1].neg()?, &xs[0]], D::Minus1)
}

impl RotaryEmbedding {
    pub(crate) fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
        let dim = cfg.rotary_ndims();
        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_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()?,
        })
    }

    pub(crate) 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)?; // (1, 1, seq_len, dim)
        let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
        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))
    }
}

#[derive(Debug)]
#[allow(clippy::upper_case_acronyms)]
struct MLP {
    gate_proj: Linear,
    up_proj: Linear,
    down_proj: Linear,
    act_fn: Activation,
    span: tracing::Span,
}

impl MLP {
    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let hidden_sz = cfg.hidden_size;
        let intermediate_sz = cfg.intermediate_size;
        let gate_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("gate_proj"))?;
        let up_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("up_proj"))?;
        let down_proj = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("down_proj"))?;
        Ok(Self {
            gate_proj,
            up_proj,
            down_proj,
            act_fn: cfg.hidden_act,
            span: tracing::span!(tracing::Level::TRACE, "mlp"),
        })
    }
}

impl Module for MLP {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
        let rhs = xs.apply(&self.up_proj)?;
        (lhs * rhs)?.apply(&self.down_proj)
    }
}

#[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)]
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_cache: bool,
    rotary_ndims: usize,
    use_flash_attn: bool,
    span: tracing::Span,
}

impl Attention {
    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let hidden_sz = cfg.hidden_size;
        let head_dim = cfg.head_dim();
        let num_heads = cfg.num_attention_heads;
        let num_kv_heads = cfg.num_key_value_heads;
        let linear_layer = if cfg.use_qkv_bias {
            linear
        } else {
            linear_no_bias
        };

        let q_proj = linear_layer(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
        let k_proj = linear_layer(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
        let v_proj = linear_layer(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: cfg.num_kv_groups(),
            head_dim,
            hidden_size: hidden_sz,
            rotary_emb,
            kv_cache: None,
            use_cache: cfg.use_cache,
            rotary_ndims: cfg.rotary_ndims(),
            use_flash_attn: cfg.use_flash_attn,
            span: tracing::span!(tracing::Level::TRACE, "attn"),
        })
    }

    fn forward(
        &mut self,
        xs: &Tensor,
        attention_mask: Option<&Tensor>,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        let _enter = self.span.enter();
        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 (rot_ndims, pass_ndims) = (self.rotary_ndims, self.head_dim - self.rotary_ndims);
        let query_rot = query_states.narrow(D::Minus1, 0, rot_ndims)?;
        let query_pass = query_states.narrow(D::Minus1, rot_ndims, pass_ndims)?;
        let key_rot = key_states.narrow(D::Minus1, 0, rot_ndims)?;
        let key_pass = key_states.narrow(D::Minus1, rot_ndims, pass_ndims)?;
        let (query_rot, key_rot) =
            self.rotary_emb
                .apply_rotary_emb_qkv(&query_rot, &key_rot, seqlen_offset)?;
        let query_states = Tensor::cat(&[query_rot, query_pass], D::Minus1)?.contiguous()?;
        let key_states = Tensor::cat(&[key_rot, key_pass], D::Minus1)?.contiguous()?;

        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)
            }
        };
        if self.use_cache {
            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 = if self.use_flash_attn {
            // flash-attn expects (b_sz, seq_len, nheads, head_dim)
            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)]
struct DecoderLayer {
    self_attn: Attention,
    mlp: MLP,
    input_layernorm: LayerNorm,
    post_attention_layernorm: LayerNorm,
    span: tracing::Span,
}

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 = candle_nn::layer_norm(
            cfg.hidden_size,
            cfg.layer_norm_eps,
            vb.pp("input_layernorm"),
        )?;
        let post_attention_layernorm = candle_nn::layer_norm(
            cfg.hidden_size,
            cfg.layer_norm_eps,
            vb.pp("post_attention_layernorm"),
        )?;
        Ok(Self {
            self_attn,
            mlp,
            input_layernorm,
            post_attention_layernorm,
            span: tracing::span!(tracing::Level::TRACE, "layer"),
        })
    }

    fn forward(
        &mut self,
        xs: &Tensor,
        attention_mask: Option<&Tensor>,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        let _enter = self.span.enter();
        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
    }
}

#[derive(Debug)]
pub struct Model {
    embed_tokens: candle_nn::Embedding,
    layers: Vec<DecoderLayer>,
    norm: LayerNorm,
    lm_head: Linear,
    device: Device,
    dtype: DType,
    span: tracing::Span,
}

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 = candle_nn::layer_norm(cfg.hidden_size, cfg.layer_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,
            device: vb.device().clone(),
            dtype: vb.dtype(),
            span: tracing::span!(tracing::Level::TRACE, "model"),
        })
    }

    fn prepare_decoder_attention_mask(
        &self,
        b_size: usize,
        tgt_len: usize,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        // Sliding window mask?
        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 _enter = self.span.enter();
        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)
    }
}