finetype-model 0.6.55

//! Transformer model for text classification.
//!
//! Architecture:
//! - Token embedding + Position embedding
//! - Transformer encoder layers
//! - Linear classification head

use candle_core::{Module, Result, Tensor};
use candle_nn::{embedding, linear, Embedding, Linear, VarBuilder};

/// Configuration for the text classifier.
#[derive(Debug, Clone)]
pub struct TextClassifierConfig {
    pub vocab_size: usize,
    pub max_seq_length: usize,
    pub d_model: usize,
    pub n_heads: usize,
    pub n_layers: usize,
    pub n_classes: usize,
    pub dropout: f64,
}

impl Default for TextClassifierConfig {
    fn default() -> Self {
        Self {
            vocab_size: 28996, // BERT vocab size
            max_seq_length: 256,
            d_model: 256,
            n_heads: 8,
            n_layers: 4,
            n_classes: 100, // Will be set based on taxonomy
            dropout: 0.1,
        }
    }
}

/// Transformer encoder layer.
pub struct TransformerEncoderLayer {
    self_attn_q: Linear,
    self_attn_k: Linear,
    self_attn_v: Linear,
    self_attn_out: Linear,
    ff1: Linear,
    ff2: Linear,
    ln1: candle_nn::LayerNorm,
    ln2: candle_nn::LayerNorm,
    n_heads: usize,
    d_model: usize,
}

impl TransformerEncoderLayer {
    pub fn new(d_model: usize, n_heads: usize, vb: VarBuilder) -> Result<Self> {
        let _head_dim = d_model / n_heads;

        let self_attn_q = linear(d_model, d_model, vb.pp("self_attn_q"))?;
        let self_attn_k = linear(d_model, d_model, vb.pp("self_attn_k"))?;
        let self_attn_v = linear(d_model, d_model, vb.pp("self_attn_v"))?;
        let self_attn_out = linear(d_model, d_model, vb.pp("self_attn_out"))?;

        let ff1 = linear(d_model, d_model * 4, vb.pp("ff1"))?;
        let ff2 = linear(d_model * 4, d_model, vb.pp("ff2"))?;

        let ln1 = candle_nn::layer_norm(d_model, 1e-5, vb.pp("ln1"))?;
        let ln2 = candle_nn::layer_norm(d_model, 1e-5, vb.pp("ln2"))?;

        Ok(Self {
            self_attn_q,
            self_attn_k,
            self_attn_v,
            self_attn_out,
            ff1,
            ff2,
            ln1,
            ln2,
            n_heads,
            d_model,
        })
    }

    pub fn forward(&self, x: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
        let (batch_size, seq_len, _) = x.dims3()?;
        let head_dim = self.d_model / self.n_heads;

        // Self-attention
        let q = self.self_attn_q.forward(x)?;
        let k = self.self_attn_k.forward(x)?;
        let v = self.self_attn_v.forward(x)?;

        // Reshape for multi-head attention
        let q = q
            .reshape((batch_size, seq_len, self.n_heads, head_dim))?
            .transpose(1, 2)?
            .contiguous()?;
        let k = k
            .reshape((batch_size, seq_len, self.n_heads, head_dim))?
            .transpose(1, 2)?
            .contiguous()?;
        let v = v
            .reshape((batch_size, seq_len, self.n_heads, head_dim))?
            .transpose(1, 2)?
            .contiguous()?;

        // Scaled dot-product attention
        let scale = (head_dim as f64).sqrt();
        let k_t = k.transpose(2, 3)?.contiguous()?;
        let attn_weights = (q.matmul(&k_t)? / scale)?;

        // Note: Mask handling simplified for initial implementation
        // TODO: Implement proper attention masking
        let _ = mask; // Suppress unused warning

        let attn_weights = candle_nn::ops::softmax(&attn_weights, 3)?;
        let attn_output = attn_weights.matmul(&v)?;

        // Reshape back
        let attn_output = attn_output.transpose(1, 2)?.contiguous()?.reshape((
            batch_size,
            seq_len,
            self.d_model,
        ))?;
        let attn_output = self.self_attn_out.forward(&attn_output)?;

        // Residual + LayerNorm
        let x = self.ln1.forward(&(x + attn_output)?)?;

        // Feed-forward
        let ff_output = self.ff1.forward(&x)?;
        let ff_output = ff_output.gelu_erf()?;
        let ff_output = self.ff2.forward(&ff_output)?;

        // Residual + LayerNorm
        let x = self.ln2.forward(&(&x + ff_output)?)?;

        Ok(x)
    }
}

/// Text classifier model.
pub struct TextClassifier {
    token_embedding: Embedding,
    position_embedding: Embedding,
    encoder_layers: Vec<TransformerEncoderLayer>,
    classifier: Linear,
    config: TextClassifierConfig,
}

impl TextClassifier {
    /// Create a new text classifier.
    pub fn new(config: TextClassifierConfig, vb: VarBuilder) -> Result<Self> {
        let token_embedding = embedding(config.vocab_size, config.d_model, vb.pp("token_emb"))?;
        let position_embedding =
            embedding(config.max_seq_length, config.d_model, vb.pp("pos_emb"))?;

        let mut encoder_layers = Vec::with_capacity(config.n_layers);
        for i in 0..config.n_layers {
            let layer = TransformerEncoderLayer::new(
                config.d_model,
                config.n_heads,
                vb.pp(format!("encoder.{}", i)),
            )?;
            encoder_layers.push(layer);
        }

        let classifier = linear(config.d_model, config.n_classes, vb.pp("classifier"))?;

        Ok(Self {
            token_embedding,
            position_embedding,
            encoder_layers,
            classifier,
            config,
        })
    }

    /// Forward pass for training.
    pub fn forward(&self, input_ids: &Tensor, attention_mask: Option<&Tensor>) -> Result<Tensor> {
        let (batch_size, seq_len) = input_ids.dims2()?;
        let device = input_ids.device();

        // Token embeddings
        let token_emb = self.token_embedding.forward(input_ids)?;

        // Position embeddings
        let positions = Tensor::arange(0u32, seq_len as u32, device)?
            .unsqueeze(0)?
            .expand((batch_size, seq_len))?;
        let pos_emb = self.position_embedding.forward(&positions)?;

        // Combine embeddings
        let mut hidden = (token_emb + pos_emb)?;
        hidden = (hidden / 2.0)?; // Average as in original

        // Apply encoder layers
        for layer in &self.encoder_layers {
            hidden = layer.forward(&hidden, attention_mask)?;
        }

        // Take CLS token (first position) for classification
        let cls_output = hidden.narrow(1, 0, 1)?.squeeze(1)?.contiguous()?;

        // Classification head
        let logits = self.classifier.forward(&cls_output)?.contiguous()?;

        Ok(logits)
    }

    /// Inference with softmax probabilities.
    pub fn infer(&self, input_ids: &Tensor, attention_mask: Option<&Tensor>) -> Result<Tensor> {
        let logits = self.forward(input_ids, attention_mask)?;
        candle_nn::ops::softmax(&logits, 1)
    }

    /// Get the model configuration.
    pub fn config(&self) -> &TextClassifierConfig {
        &self.config
    }
}