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Diffstat (limited to 'makima/src/tts/qwen3/model.rs')
| -rw-r--r-- | makima/src/tts/qwen3/model.rs | 581 |
1 files changed, 581 insertions, 0 deletions
diff --git a/makima/src/tts/qwen3/model.rs b/makima/src/tts/qwen3/model.rs new file mode 100644 index 0000000..551893b --- /dev/null +++ b/makima/src/tts/qwen3/model.rs @@ -0,0 +1,581 @@ +//! Qwen3 Language Model transformer backbone. +//! +//! Implements the 28-layer transformer with: +//! - Rotary Position Embeddings (RoPE) +//! - Grouped Query Attention (GQA) — 16 heads, 8 KV heads +//! - SiLU-gated MLP +//! - RMS normalization +//! - KV cache for autoregressive generation +//! +//! Based on the candle-transformers Qwen2 model architecture, +//! extended for Qwen3-TTS. + +use candle_core::{DType, Device, IndexOp, Module, Result, Tensor, D}; +use candle_nn::{embedding, linear_no_bias, rms_norm, Embedding, Linear, RmsNorm, VarBuilder}; + +use super::config::Qwen3LmConfig; + +// --------------------------------------------------------------------------- +// Rotary Position Embeddings +// --------------------------------------------------------------------------- + +/// Precomputed RoPE sin/cos tables. +#[derive(Debug, Clone)] +pub struct RotaryEmbedding { + cos: Tensor, + sin: Tensor, +} + +impl RotaryEmbedding { + pub fn new(config: &Qwen3LmConfig, dtype: DType, device: &Device) -> Result<Self> { + let head_dim = config.head_dim; + let max_seq = config.max_position_embeddings; + let theta = config.rope_theta; + + let inv_freq: Vec<f32> = (0..head_dim) + .step_by(2) + .map(|i| 1.0 / (theta as f32).powf(i as f32 / head_dim as f32)) + .collect(); + + let inv_freq_tensor = + Tensor::from_vec(inv_freq, (head_dim / 2,), device)?.to_dtype(DType::F32)?; + + let positions: Vec<f32> = (0..max_seq).map(|p| p as f32).collect(); + let positions_tensor = Tensor::from_vec(positions, (max_seq, 1), device)?; + + // [max_seq, head_dim/2] + let freqs = positions_tensor.matmul(&inv_freq_tensor.unsqueeze(0)?)?; + // [max_seq, head_dim] by repeating + let emb = Tensor::cat(&[&freqs, &freqs], D::Minus1)?; + + let cos = emb.cos()?.to_dtype(dtype)?; + let sin = emb.sin()?.to_dtype(dtype)?; + + Ok(Self { cos, sin }) + } + + /// Apply RoPE to query and key tensors. + /// Input shape: [batch, heads, seq_len, head_dim] + pub fn apply(&self, q: &Tensor, k: &Tensor, offset: usize) -> Result<(Tensor, Tensor)> { + let seq_len = q.dim(2)?; + let cos = self.cos.narrow(0, offset, seq_len)?; + let sin = self.sin.narrow(0, offset, seq_len)?; + + let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // [1, 1, seq, dim] + let sin = sin.unsqueeze(0)?.unsqueeze(0)?; + + let q_rotated = Self::rotate_half(q, &cos, &sin)?; + let k_rotated = Self::rotate_half(k, &cos, &sin)?; + + Ok((q_rotated, k_rotated)) + } + + fn rotate_half(x: &Tensor, cos: &Tensor, sin: &Tensor) -> Result<Tensor> { + let half_dim = x.dim(D::Minus1)? / 2; + let x1 = x.narrow(D::Minus1, 0, half_dim)?; + let x2 = x.narrow(D::Minus1, half_dim, half_dim)?; + + // [-x2, x1] concatenated + let neg_x2 = x2.neg()?; + let rotated = Tensor::cat(&[&neg_x2, &x1], D::Minus1)?; + + // x * cos + rotated * sin + let result = x.broadcast_mul(cos)?.broadcast_add(&rotated.broadcast_mul(sin)?)?; + Ok(result) + } +} + +// --------------------------------------------------------------------------- +// KV Cache +// --------------------------------------------------------------------------- + +/// Per-layer key-value cache for autoregressive generation. +#[derive(Debug, Clone)] +pub struct KvCache { + key: Option<Tensor>, + value: Option<Tensor>, +} + +impl KvCache { + pub fn new() -> Self { + Self { + key: None, + value: None, + } + } + + /// Append new key/value tensors and return the full cached sequence. + /// Input shapes: [batch, num_kv_heads, new_seq_len, head_dim] + pub fn append(&mut self, key: &Tensor, value: &Tensor) -> Result<(Tensor, Tensor)> { + let (full_key, full_value) = match (&self.key, &self.value) { + (Some(prev_k), Some(prev_v)) => { + let k = Tensor::cat(&[prev_k, key], 2)?; + let v = Tensor::cat(&[prev_v, value], 2)?; + (k, v) + } + _ => (key.clone(), value.clone()), + }; + + self.key = Some(full_key.clone()); + self.value = Some(full_value.clone()); + + Ok((full_key, full_value)) + } + + /// Current cached sequence length. + pub fn seq_len(&self) -> usize { + self.key + .as_ref() + .map(|k| k.dim(2).unwrap_or(0)) + .unwrap_or(0) + } + + /// Reset the cache. + pub fn reset(&mut self) { + self.key = None; + self.value = None; + } +} + +// --------------------------------------------------------------------------- +// Attention +// --------------------------------------------------------------------------- + +/// Multi-head attention with GQA and RoPE. +pub struct Qwen3Attention { + 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, + head_dim: usize, + num_kv_groups: usize, +} + +impl Qwen3Attention { + pub fn new(config: &Qwen3LmConfig, vb: VarBuilder) -> Result<Self> { + let hidden = config.hidden_size; + let num_heads = config.num_attention_heads; + let num_kv_heads = config.num_key_value_heads; + let head_dim = config.head_dim; + + let q_proj = linear_no_bias(hidden, num_heads * head_dim, vb.pp("q_proj"))?; + let k_proj = linear_no_bias(hidden, num_kv_heads * head_dim, vb.pp("k_proj"))?; + let v_proj = linear_no_bias(hidden, num_kv_heads * head_dim, vb.pp("v_proj"))?; + let o_proj = linear_no_bias(num_heads * head_dim, hidden, vb.pp("o_proj"))?; + + let q_norm = rms_norm(head_dim, config.rms_norm_eps, vb.pp("q_norm"))?; + let k_norm = rms_norm(head_dim, config.rms_norm_eps, vb.pp("k_norm"))?; + + Ok(Self { + q_proj, + k_proj, + v_proj, + o_proj, + q_norm, + k_norm, + num_heads, + num_kv_heads, + head_dim, + num_kv_groups: config.num_kv_groups(), + }) + } + + /// Forward pass with KV cache and RoPE. + /// Input: [batch, seq_len, hidden_size] + /// Returns: [batch, seq_len, hidden_size] + pub fn forward( + &self, + hidden_states: &Tensor, + rope: &RotaryEmbedding, + kv_cache: &mut KvCache, + attention_mask: Option<&Tensor>, + ) -> Result<Tensor> { + let (batch, seq_len, _) = hidden_states.dims3()?; + let offset = kv_cache.seq_len(); + + // Project Q, K, V + let q = self.q_proj.forward(hidden_states)?; + let k = self.k_proj.forward(hidden_states)?; + let v = self.v_proj.forward(hidden_states)?; + + // Reshape: [batch, seq, heads*dim] -> [batch, heads, seq, dim] + let q = q + .reshape((batch, seq_len, self.num_heads, self.head_dim))? + .transpose(1, 2)?; + let k = k + .reshape((batch, seq_len, self.num_kv_heads, self.head_dim))? + .transpose(1, 2)?; + let v = v + .reshape((batch, seq_len, self.num_kv_heads, self.head_dim))? + .transpose(1, 2)?; + + // Apply QK normalization (Qwen3 specific) + let q = self.apply_head_norm(&q, &self.q_norm)?; + let k = self.apply_head_norm(&k, &self.k_norm)?; + + // Apply RoPE + let (q, k) = rope.apply(&q, &k, offset)?; + + // Update KV cache + let (k, v) = kv_cache.append(&k, &v)?; + + // Expand KV heads for GQA: [batch, kv_heads, seq, dim] -> [batch, heads, seq, dim] + let k = self.repeat_kv(&k)?; + let v = self.repeat_kv(&v)?; + + // Scaled dot-product attention + let scale = (self.head_dim as f64).sqrt(); + let attn_weights = (q.matmul(&k.transpose(D::Minus2, D::Minus1)?)? / scale)?; + + let attn_weights = match attention_mask { + Some(mask) => attn_weights.broadcast_add(mask)?, + None => attn_weights, + }; + + let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?; + + // Attention output + let attn_output = attn_weights.matmul(&v)?; + + // [batch, heads, seq, dim] -> [batch, seq, heads*dim] + let attn_output = attn_output + .transpose(1, 2)? + .reshape((batch, seq_len, self.num_heads * self.head_dim))?; + + self.o_proj.forward(&attn_output) + } + + /// Apply RMS norm per-head. + fn apply_head_norm(&self, x: &Tensor, norm: &RmsNorm) -> Result<Tensor> { + let (b, h, s, d) = x.dims4()?; + // Reshape to [b*h*s, d] for norm, then back + let flat = x.reshape((b * h * s, d))?; + let normed = norm.forward(&flat)?; + normed.reshape((b, h, s, d)) + } + + /// Repeat KV heads for GQA. + fn repeat_kv(&self, x: &Tensor) -> Result<Tensor> { + if self.num_kv_groups == 1 { + return Ok(x.clone()); + } + let (batch, num_kv_heads, seq_len, head_dim) = x.dims4()?; + let x = x + .unsqueeze(2)? + .expand((batch, num_kv_heads, self.num_kv_groups, seq_len, head_dim))? + .reshape((batch, self.num_heads, seq_len, head_dim))?; + Ok(x) + } +} + +// --------------------------------------------------------------------------- +// MLP +// --------------------------------------------------------------------------- + +/// SiLU-gated feed-forward network. +pub struct Qwen3Mlp { + gate_proj: Linear, + up_proj: Linear, + down_proj: Linear, +} + +impl Qwen3Mlp { + pub fn new(config: &Qwen3LmConfig, vb: VarBuilder) -> Result<Self> { + let hidden = config.hidden_size; + let intermediate = config.intermediate_size; + + let gate_proj = linear_no_bias(hidden, intermediate, vb.pp("gate_proj"))?; + let up_proj = linear_no_bias(hidden, intermediate, vb.pp("up_proj"))?; + let down_proj = linear_no_bias(intermediate, hidden, vb.pp("down_proj"))?; + + Ok(Self { + gate_proj, + up_proj, + down_proj, + }) + } + + pub fn forward(&self, x: &Tensor) -> Result<Tensor> { + let gate = self.gate_proj.forward(x)?; + let gate = candle_nn::Activation::Silu.forward(&gate)?; + let up = self.up_proj.forward(x)?; + let hidden = (gate * up)?; + self.down_proj.forward(&hidden) + } +} + +// --------------------------------------------------------------------------- +// Transformer Layer +// --------------------------------------------------------------------------- + +/// A single Qwen3 transformer decoder layer. +pub struct Qwen3DecoderLayer { + self_attn: Qwen3Attention, + mlp: Qwen3Mlp, + input_layernorm: RmsNorm, + post_attention_layernorm: RmsNorm, +} + +impl Qwen3DecoderLayer { + pub fn new(config: &Qwen3LmConfig, vb: VarBuilder) -> Result<Self> { + let self_attn = Qwen3Attention::new(config, vb.pp("self_attn"))?; + let mlp = Qwen3Mlp::new(config, vb.pp("mlp"))?; + let input_layernorm = + rms_norm(config.hidden_size, config.rms_norm_eps, vb.pp("input_layernorm"))?; + let post_attention_layernorm = rms_norm( + config.hidden_size, + config.rms_norm_eps, + vb.pp("post_attention_layernorm"), + )?; + + Ok(Self { + self_attn, + mlp, + input_layernorm, + post_attention_layernorm, + }) + } + + pub fn forward( + &self, + hidden_states: &Tensor, + rope: &RotaryEmbedding, + kv_cache: &mut KvCache, + attention_mask: Option<&Tensor>, + ) -> Result<Tensor> { + // Pre-norm attention + let residual = hidden_states; + let hidden_states = self.input_layernorm.forward(hidden_states)?; + let hidden_states = + self.self_attn + .forward(&hidden_states, rope, kv_cache, attention_mask)?; + let hidden_states = (residual + hidden_states)?; + + // Pre-norm MLP + let residual = &hidden_states; + let hidden_states = self.post_attention_layernorm.forward(&hidden_states)?; + let hidden_states = self.mlp.forward(&hidden_states)?; + let output = (residual + hidden_states)?; + + Ok(output) + } +} + +// --------------------------------------------------------------------------- +// Full Model +// --------------------------------------------------------------------------- + +/// The complete Qwen3 language model for TTS. +/// +/// Architecture: +/// - Token embedding layer +/// - 28 transformer decoder layers +/// - Final RMS normalization +/// - LM head (projects to vocab) +pub struct Qwen3Model { + embed_tokens: Embedding, + layers: Vec<Qwen3DecoderLayer>, + norm: RmsNorm, + lm_head: Linear, + rope: RotaryEmbedding, + config: Qwen3LmConfig, + /// Last hidden states (before lm_head), used by code predictor. + last_hidden: std::cell::RefCell<Option<Tensor>>, +} + +impl Qwen3Model { + pub fn new(config: &Qwen3LmConfig, vb: VarBuilder) -> Result<Self> { + let model_vb = vb.pp("model"); + + let embed_tokens = embedding(config.vocab_size, config.hidden_size, model_vb.pp("embed_tokens"))?; + + let mut layers = Vec::with_capacity(config.num_hidden_layers); + for i in 0..config.num_hidden_layers { + let layer = Qwen3DecoderLayer::new(config, model_vb.pp(format!("layers.{i}")))?; + layers.push(layer); + } + + let norm = rms_norm(config.hidden_size, config.rms_norm_eps, model_vb.pp("norm"))?; + + // LM head — may or may not share weights with embed_tokens + let lm_head = linear_no_bias(config.hidden_size, config.vocab_size, vb.pp("lm_head"))?; + + let dtype = vb.dtype(); + let device = vb.device().clone(); + let rope = RotaryEmbedding::new(config, dtype, &device)?; + + Ok(Self { + embed_tokens, + layers, + norm, + lm_head, + rope, + config: config.clone(), + last_hidden: std::cell::RefCell::new(None), + }) + } + + /// Forward pass through the full model. + /// + /// `input_ids`: [batch, seq_len] — token IDs + /// `kv_caches`: per-layer KV caches + /// `attention_mask`: optional causal mask [batch, 1, seq_len, total_seq_len] + /// + /// Returns logits: [batch, seq_len, vocab_size] + pub fn forward( + &self, + input_ids: &Tensor, + kv_caches: &mut [KvCache], + attention_mask: Option<&Tensor>, + ) -> Result<Tensor> { + let mut hidden_states = self.embed_tokens.forward(input_ids)?; + + for (i, layer) in self.layers.iter().enumerate() { + hidden_states = + layer.forward(&hidden_states, &self.rope, &mut kv_caches[i], attention_mask)?; + } + + hidden_states = self.norm.forward(&hidden_states)?; + + // Store last hidden state for code predictor + *self.last_hidden.borrow_mut() = Some(hidden_states.clone()); + + let logits = self.lm_head.forward(&hidden_states)?; + Ok(logits) + } + + /// Forward pass with pre-computed embeddings (for first iteration where + /// text embeddings are concatenated with audio features). + /// + /// `inputs_embeds`: [batch, seq_len, hidden_size] + pub fn forward_embeds( + &self, + inputs_embeds: &Tensor, + kv_caches: &mut [KvCache], + attention_mask: Option<&Tensor>, + ) -> Result<Tensor> { + let mut hidden_states = inputs_embeds.clone(); + + for (i, layer) in self.layers.iter().enumerate() { + hidden_states = + layer.forward(&hidden_states, &self.rope, &mut kv_caches[i], attention_mask)?; + } + + hidden_states = self.norm.forward(&hidden_states)?; + + *self.last_hidden.borrow_mut() = Some(hidden_states.clone()); + + let logits = self.lm_head.forward(&hidden_states)?; + Ok(logits) + } + + /// Get the last hidden states (for the code predictor). + pub fn last_hidden_state(&self) -> Option<Tensor> { + self.last_hidden.borrow().clone() + } + + /// Number of transformer layers. + pub fn num_layers(&self) -> usize { + self.config.num_hidden_layers + } + + /// Hidden size. + pub fn hidden_size(&self) -> usize { + self.config.hidden_size + } + + /// Get token embedding layer (for input preparation). + pub fn embed_tokens(&self) -> &Embedding { + &self.embed_tokens + } + + /// Create a causal attention mask. + pub fn make_causal_mask( + seq_len: usize, + past_len: usize, + dtype: DType, + device: &Device, + ) -> Result<Tensor> { + let total_len = past_len + seq_len; + + if seq_len == 1 { + // Single token: no masking needed (can attend to everything) + return Tensor::zeros((1, 1, 1, total_len), dtype, device); + } + + // Full causal mask: lower triangular + let mask: Vec<f32> = (0..seq_len) + .flat_map(|i| { + (0..total_len).map(move |j| { + if j <= past_len + i { + 0.0 + } else { + f32::NEG_INFINITY + } + }) + }) + .collect(); + + Tensor::from_vec(mask, (1, 1, seq_len, total_len), device)?.to_dtype(dtype) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_kv_cache() { + let device = Device::Cpu; + let mut cache = KvCache::new(); + assert_eq!(cache.seq_len(), 0); + + let k = Tensor::zeros((1, 8, 5, 128), DType::F32, &device).unwrap(); + let v = Tensor::zeros((1, 8, 5, 128), DType::F32, &device).unwrap(); + let (fk, _fv) = cache.append(&k, &v).unwrap(); + assert_eq!(cache.seq_len(), 5); + assert_eq!(fk.dim(2).unwrap(), 5); + + let k2 = Tensor::zeros((1, 8, 1, 128), DType::F32, &device).unwrap(); + let v2 = Tensor::zeros((1, 8, 1, 128), DType::F32, &device).unwrap(); + let (fk2, _fv2) = cache.append(&k2, &v2).unwrap(); + assert_eq!(cache.seq_len(), 6); + assert_eq!(fk2.dim(2).unwrap(), 6); + + cache.reset(); + assert_eq!(cache.seq_len(), 0); + } + + #[test] + fn test_causal_mask_single_token() { + let mask = Qwen3Model::make_causal_mask(1, 10, DType::F32, &Device::Cpu).unwrap(); + assert_eq!(mask.dims(), &[1, 1, 1, 11]); + // All zeros — single token can attend to everything + let sum: f32 = mask.sum_all().unwrap().to_scalar().unwrap(); + assert_eq!(sum, 0.0); + } + + #[test] + fn test_causal_mask_multi_token() { + let mask = Qwen3Model::make_causal_mask(3, 0, DType::F32, &Device::Cpu).unwrap(); + assert_eq!(mask.dims(), &[1, 1, 3, 3]); + // Upper triangle should be -inf + let data: Vec<f32> = mask.flatten_all().unwrap().to_vec1().unwrap(); + // Row 0: [0, -inf, -inf] + assert_eq!(data[0], 0.0); + assert!(data[1].is_infinite() && data[1] < 0.0); + assert!(data[2].is_infinite() && data[2] < 0.0); + // Row 1: [0, 0, -inf] + assert_eq!(data[3], 0.0); + assert_eq!(data[4], 0.0); + assert!(data[5].is_infinite() && data[5] < 0.0); + // Row 2: [0, 0, 0] + assert_eq!(data[6], 0.0); + assert_eq!(data[7], 0.0); + assert_eq!(data[8], 0.0); + } +} |