candle-transformers 0.10.2

//! Z-Image Text Encoder (Qwen3 Adapter)
//!
//! This module provides a Qwen3-based text encoder for Z-Image.
//! Key difference from the standard Qwen3 model:
//! - Returns the **second-to-last layer** hidden states (hidden_states[-2])
//! - Does NOT apply the final RMSNorm

use crate::models::with_tracing::{linear_b, Linear, RmsNorm};
use candle::{DType, Device, Module, Result, Tensor};
use candle_nn::{Activation, VarBuilder};
use std::sync::Arc;

/// Text Encoder configuration (Qwen3-based)
#[derive(Debug, Clone, serde::Deserialize)]
pub struct TextEncoderConfig {
    #[serde(default = "default_vocab_size")]
    pub vocab_size: usize,
    #[serde(default = "default_hidden_size")]
    pub hidden_size: usize,
    #[serde(default = "default_intermediate_size")]
    pub intermediate_size: usize,
    #[serde(default = "default_num_hidden_layers")]
    pub num_hidden_layers: usize,
    #[serde(default = "default_num_attention_heads")]
    pub num_attention_heads: usize,
    #[serde(default = "default_num_key_value_heads")]
    pub num_key_value_heads: usize,
    #[serde(default = "default_head_dim")]
    pub head_dim: usize,
    #[serde(default = "default_rms_norm_eps")]
    pub rms_norm_eps: f64,
    #[serde(default = "default_rope_theta")]
    pub rope_theta: f64,
    #[serde(default = "default_attention_bias")]
    pub attention_bias: bool,
    #[serde(default = "default_hidden_act")]
    pub hidden_act: Activation,
    #[serde(default = "default_max_position_embeddings")]
    pub max_position_embeddings: usize,
}

fn default_vocab_size() -> usize {
    151936
}
fn default_hidden_size() -> usize {
    2560
}
fn default_intermediate_size() -> usize {
    9728
}
fn default_num_hidden_layers() -> usize {
    36
}
fn default_num_attention_heads() -> usize {
    32
}
fn default_num_key_value_heads() -> usize {
    8
}
fn default_head_dim() -> usize {
    128
}
fn default_rms_norm_eps() -> f64 {
    1e-6
}
fn default_rope_theta() -> f64 {
    1_000_000.0
}
fn default_attention_bias() -> bool {
    false
}
fn default_hidden_act() -> Activation {
    Activation::Silu
}
fn default_max_position_embeddings() -> usize {
    40960
}

impl Default for TextEncoderConfig {
    fn default() -> Self {
        Self::z_image()
    }
}

impl TextEncoderConfig {
    /// Create configuration for Z-Image Text Encoder
    pub fn z_image() -> Self {
        Self {
            vocab_size: 151936,
            hidden_size: 2560,
            intermediate_size: 9728,
            num_hidden_layers: 36,
            num_attention_heads: 32,
            num_key_value_heads: 8,
            head_dim: 128,
            rms_norm_eps: 1e-6,
            rope_theta: 1_000_000.0,
            attention_bias: false,
            hidden_act: Activation::Silu,
            max_position_embeddings: 40960,
        }
    }
}

// ==================== Rotary Embedding ====================

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

impl RotaryEmbedding {
    fn new(dtype: DType, cfg: &TextEncoderConfig, dev: &Device) -> Result<Self> {
        let dim = cfg.head_dim;
        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::F32)?;
        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
            .to_dtype(DType::F32)?
            .reshape((max_seq_len, 1))?;
        let freqs = t.matmul(&inv_freq)?;
        Ok(Self {
            sin: freqs.sin()?.to_dtype(dtype)?,
            cos: freqs.cos()?.to_dtype(dtype)?,
        })
    }

    /// Apply RoPE (q, k shape: B x H x L x D)
    fn apply(&self, q: &Tensor, k: &Tensor, offset: usize) -> Result<(Tensor, Tensor)> {
        let (_, _, seq_len, _) = q.dims4()?;
        let cos = self.cos.narrow(0, offset, seq_len)?;
        let sin = self.sin.narrow(0, offset, seq_len)?;
        let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
        let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
        Ok((q_embed, k_embed))
    }
}

// ==================== MLP ====================

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

impl Mlp {
    fn new(cfg: &TextEncoderConfig, vb: VarBuilder) -> Result<Self> {
        Ok(Self {
            gate_proj: candle_nn::linear_no_bias(
                cfg.hidden_size,
                cfg.intermediate_size,
                vb.pp("gate_proj"),
            )?,
            up_proj: candle_nn::linear_no_bias(
                cfg.hidden_size,
                cfg.intermediate_size,
                vb.pp("up_proj"),
            )?,
            down_proj: candle_nn::linear_no_bias(
                cfg.intermediate_size,
                cfg.hidden_size,
                vb.pp("down_proj"),
            )?,
            act_fn: cfg.hidden_act,
        })
    }
}

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

// ==================== Attention ====================

fn repeat_kv(x: Tensor, n_rep: usize) -> Result<Tensor> {
    if n_rep == 1 {
        Ok(x)
    } else {
        let (b_sz, n_kv_head, seq_len, head_dim) = x.dims4()?;
        x.unsqueeze(2)?
            .broadcast_as((b_sz, n_kv_head, n_rep, seq_len, head_dim))?
            .reshape((b_sz, n_kv_head * n_rep, seq_len, head_dim))
    }
}

#[derive(Debug, Clone)]
struct Attention {
    q_proj: Linear,
    k_proj: Linear,
    v_proj: Linear,
    o_proj: Linear,
    q_norm: RmsNorm,
    k_norm: RmsNorm,
    num_heads: usize,
    num_kv_heads: usize,
    num_kv_groups: usize,
    head_dim: usize,
    hidden_size: usize,
    rotary_emb: Arc<RotaryEmbedding>,
}

impl Attention {
    fn new(
        cfg: &TextEncoderConfig,
        rotary_emb: Arc<RotaryEmbedding>,
        vb: VarBuilder,
    ) -> Result<Self> {
        let head_dim = cfg.head_dim;
        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 q_proj = linear_b(
            cfg.hidden_size,
            num_heads * head_dim,
            cfg.attention_bias,
            vb.pp("q_proj"),
        )?;
        let k_proj = linear_b(
            cfg.hidden_size,
            num_kv_heads * head_dim,
            cfg.attention_bias,
            vb.pp("k_proj"),
        )?;
        let v_proj = linear_b(
            cfg.hidden_size,
            num_kv_heads * head_dim,
            cfg.attention_bias,
            vb.pp("v_proj"),
        )?;
        let o_proj = linear_b(
            num_heads * head_dim,
            cfg.hidden_size,
            cfg.attention_bias,
            vb.pp("o_proj"),
        )?;

        let q_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("q_norm"))?;
        let k_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("k_norm"))?;

        let hidden_size = head_dim * cfg.num_attention_heads;

        Ok(Self {
            q_proj,
            k_proj,
            v_proj,
            o_proj,
            q_norm,
            k_norm,
            num_heads,
            num_kv_heads,
            num_kv_groups,
            head_dim,
            hidden_size,
            rotary_emb,
        })
    }

    fn forward(&self, x: &Tensor, attn_mask: Option<&Tensor>, offset: usize) -> Result<Tensor> {
        let (b, l, _) = x.dims3()?;

        // 1. Proj
        let q = self.q_proj.forward(x)?;
        let k = self.k_proj.forward(x)?;
        let v = self.v_proj.forward(x)?;

        // 2. Reshape: (B, L, H, D) -> (B, H, L, D)
        let q = q
            .reshape((b, l, self.num_heads, self.head_dim))?
            .transpose(1, 2)?;
        let k = k
            .reshape((b, l, self.num_kv_heads, self.head_dim))?
            .transpose(1, 2)?;
        let v = v
            .reshape((b, l, self.num_kv_heads, self.head_dim))?
            .transpose(1, 2)?;

        // 3. Per-head RMSNorm
        let q_flat = q.flatten(0, 2)?;
        let k_flat = k.flatten(0, 2)?;
        let q_flat = self.q_norm.forward(&q_flat)?;
        let k_flat = self.k_norm.forward(&k_flat)?;
        let q = q_flat.reshape((b, self.num_heads, l, self.head_dim))?;
        let k = k_flat.reshape((b, self.num_kv_heads, l, self.head_dim))?;

        // 4. RoPE
        let (q, k) = self.rotary_emb.apply(&q, &k, offset)?;

        // 5. GQA repeat_kv
        let k = repeat_kv(k, self.num_kv_groups)?.contiguous()?;
        let v = repeat_kv(v, self.num_kv_groups)?.contiguous()?;

        // 6. Attention score
        let scale = 1.0 / (self.head_dim as f64).sqrt();
        let mut scores = (q.matmul(&k.transpose(2, 3)?)? * scale)?;
        if let Some(m) = attn_mask {
            scores = scores.broadcast_add(m)?;
        }
        let probs = candle_nn::ops::softmax_last_dim(&scores)?;
        let ctx = probs.matmul(&v)?; // (B, H, L, D)

        // 7. Output proj
        ctx.transpose(1, 2)?
            .reshape((b, l, self.hidden_size))?
            .apply(&self.o_proj)
    }
}

// ==================== Decoder Layer ====================

#[derive(Debug, Clone)]
struct DecoderLayer {
    self_attn: Attention,
    mlp: Mlp,
    ln1: RmsNorm,
    ln2: RmsNorm,
}

impl DecoderLayer {
    fn new(cfg: &TextEncoderConfig, rotary: Arc<RotaryEmbedding>, vb: VarBuilder) -> Result<Self> {
        let self_attn = Attention::new(cfg, rotary, vb.pp("self_attn"))?;
        let mlp = Mlp::new(cfg, vb.pp("mlp"))?;
        let ln1 = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
        let ln2 = RmsNorm::new(
            cfg.hidden_size,
            cfg.rms_norm_eps,
            vb.pp("post_attention_layernorm"),
        )?;
        Ok(Self {
            self_attn,
            mlp,
            ln1,
            ln2,
        })
    }

    fn forward(&self, x: &Tensor, mask: Option<&Tensor>, offset: usize) -> Result<Tensor> {
        let h = self.ln1.forward(x)?;
        let h = self.self_attn.forward(&h, mask, offset)?;
        let x = (x + h)?;
        let h2 = self.ln2.forward(&x)?;
        let h2 = h2.apply(&self.mlp)?;
        x + h2
    }
}

// ==================== ZImageTextEncoder ====================

/// Z-Image Text Encoder (Qwen3-based)
///
/// Returns the second-to-last layer hidden states (hidden_states[-2])
/// without applying the final RMSNorm.
#[derive(Debug, Clone)]
pub struct ZImageTextEncoder {
    embed_tokens: candle_nn::Embedding,
    layers: Vec<DecoderLayer>,
    num_hidden_layers: usize,
    device: Device,
    dtype: DType,
}

impl ZImageTextEncoder {
    pub fn new(cfg: &TextEncoderConfig, vb: VarBuilder) -> Result<Self> {
        // Note: weights have "model." prefix
        let vb_model = vb.pp("model");

        let embed_tokens =
            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_model.pp("embed_tokens"))?;

        let rotary = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb.device())?);

        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
        let vb_layers = vb_model.pp("layers");
        for i in 0..cfg.num_hidden_layers {
            layers.push(DecoderLayer::new(cfg, rotary.clone(), vb_layers.pp(i))?);
        }

        // NOTE: We do NOT load the final norm (model.norm.weight)
        // because we return the second-to-last layer output without final norm

        Ok(Self {
            embed_tokens,
            layers,
            num_hidden_layers: cfg.num_hidden_layers,
            device: vb.device().clone(),
            dtype: vb.dtype(),
        })
    }

    /// Create causal attention mask
    fn causal_mask(&self, b: usize, tgt: usize, offset: usize) -> Result<Tensor> {
        let minf = f32::NEG_INFINITY;
        let mask: Vec<_> = (0..tgt)
            .flat_map(|i| {
                (0..(tgt + offset)).map(move |j| if j <= i + offset { 0.0 } else { minf })
            })
            .collect();
        Tensor::from_slice(&mask, (b, 1, tgt, tgt + offset), &self.device)?.to_dtype(self.dtype)
    }

    /// Encode text, returning second-to-last layer hidden states
    ///
    /// # Arguments
    /// * `input_ids` - Token IDs (B, seq_len)
    ///
    /// # Returns
    /// Hidden states (B, seq_len, hidden_size) from layer[-2]
    ///
    /// **Important**: Returns raw output from layer[-2] WITHOUT final RMSNorm
    pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
        let (b, l) = input_ids.dims2()?;
        let mut hidden_states = self.embed_tokens.forward(input_ids)?;

        let causal = if l == 1 {
            None
        } else {
            Some(self.causal_mask(b, l, 0)?)
        };

        // num_hidden_layers = 36, second-to-last layer index = 34
        let target_layer = self.num_hidden_layers - 2;

        for (i, layer) in self.layers.iter().enumerate() {
            hidden_states = layer.forward(&hidden_states, causal.as_ref(), 0)?;

            // Return after second-to-last layer, do NOT apply final norm
            if i == target_layer {
                return Ok(hidden_states);
            }
        }

        // Should not reach here
        candle::bail!("Layer index out of bounds")
    }

    /// Get the output dimension (hidden_size)
    pub fn hidden_size(&self) -> usize {
        // This is derived from embed_tokens weight shape
        self.embed_tokens.embeddings().dim(1).unwrap_or(2560)
    }
}