aha/src/models/common/mod.rs

use anyhow::Result;
use candle_core::{D, Tensor};
use candle_nn::{
    Activation, Conv2d, Conv2dConfig, LayerNorm, LayerNormConfig, Linear, Module, VarBuilder,
    conv2d, conv2d_no_bias, layer_norm, linear, linear_no_bias,
};

use crate::{position_embed::rope::apply_rotary_pos_emb, utils::tensor_utils::repeat_kv};

#[derive(Debug, Clone)]
pub struct GateUpDownMLP {
    gate_proj: Linear,
    up_proj: Linear,
    down_proj: Linear,
    act_fn: Activation,
}

impl GateUpDownMLP {
    pub fn new(
        vb: VarBuilder,
        hidden_size: usize,
        intermediate_size: usize,
        act_fn: Activation,
        bias: bool,
    ) -> Result<Self> {
        let (gate_proj, up_proj, down_proj) = if bias {
            (
                linear(hidden_size, intermediate_size, vb.pp("gate_proj"))?,
                linear(hidden_size, intermediate_size, vb.pp("up_proj"))?,
                linear(intermediate_size, hidden_size, vb.pp("down_proj"))?,
            )
        } else {
            (
                linear_no_bias(hidden_size, intermediate_size, vb.pp("gate_proj"))?,
                linear_no_bias(hidden_size, intermediate_size, vb.pp("up_proj"))?,
                linear_no_bias(intermediate_size, hidden_size, vb.pp("down_proj"))?,
            )
        };
        Ok(Self {
            gate_proj,
            up_proj,
            down_proj,
            act_fn,
        })
    }
}

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

pub struct TwoLinearMLP {
    linear1: Linear,
    linear2: Linear,
    act: Activation,
}

impl TwoLinearMLP {
    pub fn new(
        vb: VarBuilder,
        embedding_dim: usize,
        mlp_dim: usize,
        act: Activation,
        bias: bool,
        linear1_pp_name: &str,
        linear2_pp_name: &str,
    ) -> Result<Self> {
        let (linear1, linear2) = if bias {
            (
                linear(embedding_dim, mlp_dim, vb.pp(linear1_pp_name))?,
                linear(mlp_dim, embedding_dim, vb.pp(linear2_pp_name))?,
            )
        } else {
            (
                linear_no_bias(embedding_dim, mlp_dim, vb.pp(linear1_pp_name))?,
                linear_no_bias(mlp_dim, embedding_dim, vb.pp(linear2_pp_name))?,
            )
        };
        Ok(Self {
            linear1,
            linear2,
            act,
        })
    }
    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let xs = xs
            .apply(&self.linear1)?
            .apply(&self.act)?
            .apply(&self.linear2)?;
        Ok(xs)
    }
}

#[derive(Debug, Clone)]
// pub struct AttentionNobias {
pub struct NaiveAttention {
    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,
    middle_size: usize,
    kv_cache: Option<(Tensor, Tensor)>,
}

// impl AttentionNobias {
impl NaiveAttention {
    pub fn new(
        vb: VarBuilder,
        hidden_size: usize,
        num_attention_heads: usize,
        num_key_value_heads: usize,
        head_dim: Option<usize>,
        bias: bool,
        o_proj_pp_name: Option<&str>,
    ) -> Result<Self> {
        let num_kv_groups = num_attention_heads / num_key_value_heads;
        let head_dim = match head_dim {
            None => hidden_size / num_attention_heads,
            Some(dim) => dim,
        };
        let o_proj_pp_name = o_proj_pp_name.unwrap_or("o_proj");
        let (q_proj, k_proj, v_proj, o_proj) = if bias {
            (
                linear(hidden_size, num_attention_heads * head_dim, vb.pp("q_proj"))?,
                linear(hidden_size, num_key_value_heads * head_dim, vb.pp("k_proj"))?,
                linear(hidden_size, num_key_value_heads * head_dim, vb.pp("v_proj"))?,
                linear(
                    num_attention_heads * head_dim,
                    hidden_size,
                    vb.pp(o_proj_pp_name),
                )?,
            )
        } else {
            (
                linear_no_bias(hidden_size, num_attention_heads * head_dim, vb.pp("q_proj"))?,
                linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("k_proj"))?,
                linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("v_proj"))?,
                linear_no_bias(
                    num_attention_heads * head_dim,
                    hidden_size,
                    vb.pp(o_proj_pp_name),
                )?,
            )
        };

        Ok(Self {
            q_proj,
            k_proj,
            v_proj,
            o_proj,
            num_heads: num_attention_heads,
            num_kv_heads: num_key_value_heads,
            num_kv_groups,
            head_dim,
            middle_size: num_attention_heads * head_dim,
            kv_cache: None,
        })
    }

    pub fn forward(
        &self,
        xs: &Tensor,
        cos: Option<&Tensor>,
        sin: Option<&Tensor>,
        attention_mask: Option<&Tensor>,
        tof32: bool,
    ) -> 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) = if let Some(cos) = cos
            && let Some(sin) = sin
        {
            apply_rotary_pos_emb(&query_states, &key_states, cos, sin, tof32)?
        } else {
            (query_states, key_states)
        };

        let scale = 1f64 / f64::sqrt(self.head_dim as f64);
        let attn_output = eager_attention_forward(
            &query_states,
            &key_states,
            &value_states,
            Some(self.num_kv_groups),
            attention_mask,
            scale,
        )?;
        let attn_output = attn_output.reshape((b_sz, q_len, self.middle_size))?;
        let attn_output = attn_output.apply(&self.o_proj)?;
        Ok(attn_output)
    }

    pub fn forward_with_cache(
        &mut self,
        xs: &Tensor,
        cos: &Tensor,
        sin: &Tensor,
        attention_mask: Option<&Tensor>,
        tof32: bool,
    ) -> 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) =
            apply_rotary_pos_emb(&query_states, &key_states, cos, sin, tof32)?;
        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 scale = 1f64 / f64::sqrt(self.head_dim as f64);
        let attn_output = eager_attention_forward(
            &query_states,
            &key_states,
            &value_states,
            Some(self.num_kv_groups),
            attention_mask,
            scale,
        )?;
        let attn_output = attn_output.reshape((b_sz, q_len, self.middle_size))?;
        let attn_output = attn_output.apply(&self.o_proj)?;
        Ok(attn_output)
    }

    pub fn clear_kv_cache(&mut self) {
        self.kv_cache = None
    }
}

pub fn eager_attention_forward(
    query_states: &Tensor,
    key_states: &Tensor,
    value_states: &Tensor,
    num_key_value_groups: Option<usize>,
    attention_mask: Option<&Tensor>,
    scaling: f64,
) -> Result<Tensor> {
    // input q shape:(b, num_head, seq_len, dim)
    // input k/v shape:(b, num_kv_head, seq_len, dim)
    let key_states = match num_key_value_groups {
        Some(g) => repeat_kv(key_states.clone(), g)?.contiguous()?,
        None => key_states.clone(),
    };
    let value_states = match num_key_value_groups {
        Some(g) => repeat_kv(value_states.clone(), g)?.contiguous()?,
        None => value_states.clone(),
    };
    let query_states = query_states.contiguous()?;
    let key_states = key_states.contiguous()?;
    let value_states = value_states.contiguous()?;
    let attn_output = {
        #[cfg(not(feature = "flash-attn"))]
        {
            let attn_weights = query_states.matmul(&key_states.transpose(D::Minus2, D::Minus1)?)?;
            let attn_weights = (attn_weights * scaling)?;
            let attn_weights = match attention_mask {
                None => attn_weights,
                Some(mask) => attn_weights.broadcast_add(&mask.to_dtype(attn_weights.dtype())?)?,
            };
            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
            attn_weights.matmul(&value_states)?
        }
        #[cfg(feature = "flash-attn")]
        {
            // use flash-attn,
            // flash-attn shape: (bs, seq_len, num_head, head_dim)
            let query_states = query_states.transpose(1, 2)?;
            let key_states = key_states.transpose(1, 2)?;
            let value_states = value_states.transpose(1, 2)?;
            let attn_output = candle_flash_attn::flash_attn(
                &query_states,
                &key_states,
                &value_states,
                scaling as f32,
                attention_mask.is_some(),
            )?
            .transpose(1, 2)?;
            attn_output
        }
    };
    //(b, n_head, seq_len, dim) -> (b, seq_len, n_head, dim)
    let attn_output = attn_output.transpose(1, 2)?.contiguous()?;

    Ok(attn_output)
}

pub fn get_conv2d(
    vb: VarBuilder,
    in_c: usize,
    out_c: usize,
    kernel_size: usize,
    padding: usize,
    stride: usize,
    dilation: usize,
    groups: usize,
    bias: bool,
) -> Result<Conv2d> {
    let cfg = Conv2dConfig {
        padding,
        stride,
        dilation,
        groups,
        cudnn_fwd_algo: None,
    };
    let conv2d = if bias {
        conv2d(in_c, out_c, kernel_size, cfg, vb)?
    } else {
        conv2d_no_bias(in_c, out_c, kernel_size, cfg, vb)?
    };
    Ok(conv2d)
}

pub fn get_layer_norm(vb: VarBuilder, eps: f64, dim: usize) -> Result<LayerNorm> {
    let ln_config = LayerNormConfig {
        eps,
        remove_mean: true, // true for layernorm, false for RMSNorm
        affine: true,      // true for with bias, false for without bias
    };
    let norm = layer_norm(dim, ln_config, vb)?;
    Ok(norm)
}