runtime/models_v2/

encodec.rs

//! EnCodec Model V2 - Neural Audio Codec
//!
//! CNN-based encoder/decoder for audio compression using residual vector quantization

use crate::model_config;
use super::traits::*;
use anyhow::Result;
use serde::{Serialize, Deserialize};

model_config!(EncodecConfig {
    vocab_size: usize = 1024,  // Same as codebook_size for compatibility
    hidden_size: usize = 128,
    num_hidden_layers: usize = 4,  // Number of upsampling stages
    audio_channels: usize = 1,
    sample_rate: usize = 24000,
    num_filters: usize = 32,
    num_residual_layers: usize = 1,
    upsampling_ratios: Vec<usize> = vec![8, 5, 4, 2],
    norm_type: String = "weight_norm".to_string(),
    codebook_size: usize = 1024,
    codebook_dim: usize = 128,
    num_codebooks: usize = 32,
    use_causal_conv: bool = true,
    pad_mode: String = "reflect".to_string(),
    bandwidth: f32 = 6.0,
    layer_norm_eps: f32 = 1e-5,
    pad_token_id: i64 = 0,
    bos_token_id: i64 = 1,
    eos_token_id: i64 = 2,
});

impl EncodecConfig {
    pub fn from_gguf_config(gguf: &crate::weight_loader_core::GGUFModelConfig) -> Self {
        Self {
            hidden_size: gguf.hidden_size,
            ..Default::default()
        }
    }
}

pub struct EncodecModelV2 {
    config: EncodecConfig,
    device: Device,
    encoder: EncodecEncoder,
    decoder: EncodecDecoder,
    quantizer: ResidualVectorQuantizer,
}

pub struct EncodecEncoder {
    conv_in: EncodecConv1d,
    down_blocks: Vec<EncodecDownsampleBlock>,
    conv_out: EncodecConv1d,
    lstm: Option<EncodecLSTM>,
}

pub struct EncodecDecoder {
    conv_in: EncodecConv1d,
    up_blocks: Vec<EncodecUpsampleBlock>,
    conv_out: EncodecConv1d,
    lstm: Option<EncodecLSTM>,
}

pub struct EncodecDownsampleBlock {
    conv_layers: Vec<EncodecConv1d>,
    downsample: EncodecConv1d,
    residual_layers: Vec<EncodecResidualUnit>,
}

pub struct EncodecUpsampleBlock {
    upsample: EncodecConvTranspose1d,
    conv_layers: Vec<EncodecConv1d>,
    residual_layers: Vec<EncodecResidualUnit>,
}

pub struct EncodecResidualUnit {
    conv1: EncodecConv1d,
    conv2: EncodecConv1d,
}

pub struct EncodecConv1d {
    weight: Tensor,
    bias: Option<Tensor>,
    in_channels: usize,
    out_channels: usize,
    kernel_size: usize,
    stride: usize,
    padding: usize,
}

pub struct EncodecConvTranspose1d {
    weight: Tensor,
    bias: Option<Tensor>,
    in_channels: usize,
    out_channels: usize,
    kernel_size: usize,
    stride: usize,
}

pub struct EncodecLSTM {
    weight_ih: Tensor,
    weight_hh: Tensor,
    bias_ih: Tensor,
    bias_hh: Tensor,
    hidden_size: usize,
    num_layers: usize,
}

pub struct ResidualVectorQuantizer {
    codebooks: Vec<Tensor>,  // [num_codebooks, codebook_size, codebook_dim]
    codebook_size: usize,
    codebook_dim: usize,
    num_codebooks: usize,
}

impl Model for EncodecModelV2 {
    type Config = EncodecConfig;

    fn new(config: EncodecConfig) -> Result<Self> {
        let device = Device::CPU;

        let encoder = EncodecEncoder::new(&config, &device)?;
        let decoder = EncodecDecoder::new(&config, &device)?;
        let quantizer = ResidualVectorQuantizer::new(&config, &device)?;

        Ok(Self { config, device, encoder, decoder, quantizer })
    }

    fn from_weights(config: EncodecConfig, weights: ModelWeights) -> Result<Self> {
        let model = Self::new(config)?;
        // Weight loading would be implemented here
        Ok(model)
    }

    fn forward(&self, inputs: &ModelInputs) -> Result<ModelOutputs> {
        match inputs {
            ModelInputs::Audio { input_features, .. } => {
                // Encode: audio -> latent codes
                let encoded = self.encoder.forward(input_features)?;

                // Quantize: continuous latents -> discrete codes
                let (quantized, codes) = self.quantizer.forward(&encoded)?;

                // Decode: discrete codes -> reconstructed audio
                let decoded = self.decoder.forward(&quantized)?;

                // Return encoded representation
                Ok(ModelOutputs::Logits {
                    logits: decoded,
                    hidden_states: Some(encoded)
                })
            }
            _ => Err(anyhow::anyhow!("Encodec requires audio input")),
        }
    }

    fn generate(&self, _prompt: &str, _config: &GenerationConfig) -> Result<String> {
        Err(anyhow::anyhow!("Encodec is a codec model, use encode/decode methods"))
    }

    fn config(&self) -> &Self::Config { &self.config }

    fn memory_requirements(&self) -> MemoryRequirements {
        let p = self.config.hidden_size * self.config.num_filters * 1000 * 4;
        MemoryRequirements { gpu_memory: p, cpu_memory: p / 4, kv_cache_memory: 0, peak_memory: p * 2 }
    }

    fn to_device(&mut self, device: &Device) -> Result<()> {
        self.device = device.clone();
        Ok(())
    }
}

impl EncodecEncoder {
    fn new(config: &EncodecConfig, device: &Device) -> Result<Self> {
        let mut channels = config.num_filters;

        // Initial conv: audio_channels -> num_filters
        let conv_in = EncodecConv1d::new(config.audio_channels, channels, 7, 1, 3, device)?;

        // Downsample blocks
        let mut down_blocks = Vec::new();
        for &ratio in &config.upsampling_ratios {
            let out_channels = channels * 2;
            down_blocks.push(EncodecDownsampleBlock::new(
                channels, out_channels, ratio, config.num_residual_layers, device
            )?);
            channels = out_channels;
        }

        // Output conv
        let conv_out = EncodecConv1d::new(channels, config.hidden_size, 7, 1, 3, device)?;

        // Optional LSTM for sequential modeling
        let lstm = Some(EncodecLSTM::new(config.hidden_size, 2, device)?);

        Ok(Self { conv_in, down_blocks, conv_out, lstm })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = self.conv_in.forward(input)?;
        x = elu(&x)?;

        for block in &self.down_blocks {
            x = block.forward(&x)?;
        }

        x = self.conv_out.forward(&x)?;

        if let Some(ref lstm) = self.lstm {
            x = lstm.forward(&x)?;
        }

        Ok(x)
    }
}

// ELU activation: max(0, x) + min(0, alpha * (exp(x) - 1))
fn elu(input: &Tensor) -> Result<Tensor> {
    let x = input.to_candle()?;
    let result = x.elu(1.0)?;
    Ok(Tensor::from_candle(result))
}

impl EncodecDecoder {
    fn new(config: &EncodecConfig, device: &Device) -> Result<Self> {
        let mut channels = config.hidden_size;

        // Initial conv
        let conv_in = EncodecConv1d::new(config.hidden_size, channels, 7, 1, 3, device)?;

        // Optional LSTM
        let lstm = Some(EncodecLSTM::new(channels, 2, device)?);

        // Upsample blocks (reverse order of downsampling)
        let mut up_blocks = Vec::new();
        let ratios: Vec<_> = config.upsampling_ratios.iter().rev().copied().collect();

        for (i, &ratio) in ratios.iter().enumerate() {
            let out_channels = if i == ratios.len() - 1 {
                config.num_filters
            } else {
                channels / 2
            };
            up_blocks.push(EncodecUpsampleBlock::new(
                channels, out_channels, ratio, config.num_residual_layers, device
            )?);
            channels = out_channels;
        }

        // Output conv: num_filters -> audio_channels
        let conv_out = EncodecConv1d::new(config.num_filters, config.audio_channels, 7, 1, 3, device)?;

        Ok(Self { conv_in, up_blocks, conv_out, lstm })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = self.conv_in.forward(input)?;

        if let Some(ref lstm) = self.lstm {
            x = lstm.forward(&x)?;
        }

        x = elu(&x)?;

        for block in &self.up_blocks {
            x = block.forward(&x)?;
        }

        self.conv_out.forward(&x)
    }
}

impl EncodecDownsampleBlock {
    fn new(in_channels: usize, out_channels: usize, ratio: usize, num_residual: usize, device: &Device) -> Result<Self> {
        let mut conv_layers = Vec::new();
        let mut residual_layers = Vec::new();

        // Residual units before downsampling
        for _ in 0..num_residual {
            residual_layers.push(EncodecResidualUnit::new(in_channels, device)?);
        }

        // Pre-downsample conv
        conv_layers.push(EncodecConv1d::new(in_channels, in_channels, 3, 1, 1, device)?);

        // Downsample conv with stride
        let downsample = EncodecConv1d::new(in_channels, out_channels, ratio * 2, ratio, ratio / 2, device)?;

        Ok(Self { conv_layers, downsample, residual_layers })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = input.clone();

        for layer in &self.residual_layers {
            x = layer.forward(&x)?;
        }

        for conv in &self.conv_layers {
            x = elu(&conv.forward(&x)?)?;
        }

        self.downsample.forward(&x)
    }
}

impl EncodecUpsampleBlock {
    fn new(in_channels: usize, out_channels: usize, ratio: usize, num_residual: usize, device: &Device) -> Result<Self> {
        // Upsample with transposed conv
        let upsample = EncodecConvTranspose1d::new(in_channels, out_channels, ratio * 2, ratio, device)?;

        let mut conv_layers = Vec::new();
        let mut residual_layers = Vec::new();

        // Post-upsample conv
        conv_layers.push(EncodecConv1d::new(out_channels, out_channels, 3, 1, 1, device)?);

        // Residual units after upsampling
        for _ in 0..num_residual {
            residual_layers.push(EncodecResidualUnit::new(out_channels, device)?);
        }

        Ok(Self { upsample, conv_layers, residual_layers })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let mut x = self.upsample.forward(input)?;

        for conv in &self.conv_layers {
            x = elu(&conv.forward(&x)?)?;
        }

        for layer in &self.residual_layers {
            x = layer.forward(&x)?;
        }

        Ok(x)
    }
}

impl EncodecResidualUnit {
    fn new(channels: usize, device: &Device) -> Result<Self> {
        let conv1 = EncodecConv1d::new(channels, channels, 3, 1, 1, device)?;
        let conv2 = EncodecConv1d::new(channels, channels, 1, 1, 0, device)?;
        Ok(Self { conv1, conv2 })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let x = elu(&self.conv1.forward(input)?)?;
        let x = self.conv2.forward(&x)?;
        ops_fn::add(input, &x)
    }
}

impl EncodecConv1d {
    fn new(in_channels: usize, out_channels: usize, kernel_size: usize, stride: usize, padding: usize, device: &Device) -> Result<Self> {
        let weight = ops_fn::zeros(&[out_channels, in_channels, kernel_size], DataType::Float32, device)?;
        let bias = Some(ops_fn::zeros(&[out_channels], DataType::Float32, device)?);
        Ok(Self { weight, bias, in_channels, out_channels, kernel_size, stride, padding })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let out = ops_fn::conv1d(input, &self.weight, None, self.stride, self.padding)?;

        if let Some(ref bias) = self.bias {
            let bias_candle = bias.to_candle()?;
            let out_candle = out.to_candle()?;
            let bias_expanded = bias_candle.reshape(&[1, self.out_channels, 1])?;
            Ok(Tensor::from_candle(out_candle.broadcast_add(&bias_expanded)?))
        } else {
            Ok(out)
        }
    }
}

impl EncodecConvTranspose1d {
    fn new(in_channels: usize, out_channels: usize, kernel_size: usize, stride: usize, device: &Device) -> Result<Self> {
        let weight = ops_fn::zeros(&[in_channels, out_channels, kernel_size], DataType::Float32, device)?;
        let bias = Some(ops_fn::zeros(&[out_channels], DataType::Float32, device)?);
        Ok(Self { weight, bias, in_channels, out_channels, kernel_size, stride })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Simplified transposed conv - would use proper conv_transpose1d in full impl
        let input_candle = input.to_candle()?;
        let weight_candle = self.weight.to_candle()?;

        // For now, use conv with modified dimensions (placeholder)
        // Real implementation would use conv_transpose1d kernel
        let shape = input_candle.shape().dims();
        let (batch, _, seq_len) = (shape[0], shape[1], shape[2]);
        let out_len = seq_len * self.stride;

        // Create output with upsampled size
        let device = input_candle.device();
        let output = candle_core::Tensor::zeros(&[batch, self.out_channels, out_len], candle_core::DType::F32, device)?;

        if let Some(ref bias) = self.bias {
            let bias_candle = bias.to_candle()?;
            let bias_expanded = bias_candle.reshape(&[1, self.out_channels, 1])?;
            Ok(Tensor::from_candle(output.broadcast_add(&bias_expanded)?))
        } else {
            Ok(Tensor::from_candle(output))
        }
    }
}

impl EncodecLSTM {
    fn new(hidden_size: usize, num_layers: usize, device: &Device) -> Result<Self> {
        let weight_ih = ops_fn::zeros(&[4 * hidden_size, hidden_size], DataType::Float32, device)?;
        let weight_hh = ops_fn::zeros(&[4 * hidden_size, hidden_size], DataType::Float32, device)?;
        let bias_ih = ops_fn::zeros(&[4 * hidden_size], DataType::Float32, device)?;
        let bias_hh = ops_fn::zeros(&[4 * hidden_size], DataType::Float32, device)?;
        Ok(Self { weight_ih, weight_hh, bias_ih, bias_hh, hidden_size, num_layers })
    }

    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Simplified LSTM: just pass through for now
        // Real implementation would do full LSTM computation
        Ok(input.clone())
    }
}

impl ResidualVectorQuantizer {
    fn new(config: &EncodecConfig, device: &Device) -> Result<Self> {
        let mut codebooks = Vec::with_capacity(config.num_codebooks);

        for _ in 0..config.num_codebooks {
            codebooks.push(ops_fn::zeros(
                &[config.codebook_size, config.codebook_dim],
                DataType::Float32,
                device
            )?);
        }

        Ok(Self {
            codebooks,
            codebook_size: config.codebook_size,
            codebook_dim: config.codebook_dim,
            num_codebooks: config.num_codebooks,
        })
    }

    fn forward(&self, input: &Tensor) -> Result<(Tensor, Vec<Tensor>)> {
        let mut residual = input.clone();
        let input_candle = input.to_candle()?;
        let mut quantized = Tensor::from_candle(input_candle.zeros_like()?);
        let mut codes = Vec::with_capacity(self.num_codebooks);

        for codebook in &self.codebooks {
            // Find nearest codebook entry
            let (code_indices, quantized_part) = self.quantize_residual(&residual, codebook)?;

            // Accumulate quantized values
            quantized = ops_fn::add(&quantized, &quantized_part)?;

            // Update residual
            residual = ops_fn::sub(&residual, &quantized_part)?;

            codes.push(code_indices);
        }

        Ok((quantized, codes))
    }

    fn quantize_residual(&self, input: &Tensor, codebook: &Tensor) -> Result<(Tensor, Tensor)> {
        let input_candle = input.to_candle()?;
        let codebook_candle = codebook.to_candle()?;

        let shape = input_candle.shape().dims();
        let (batch, channels, seq_len) = (shape[0], shape[1], shape[2]);

        // Reshape input: [batch, channels, seq] -> [batch * seq, channels]
        let input_flat = input_candle.transpose(1, 2)?.reshape(&[batch * seq_len, channels])?;

        // Compute distances to codebook entries
        // distance = ||x - c||^2 = ||x||^2 - 2<x,c> + ||c||^2
        let input_sq = input_flat.sqr()?.sum(1)?;
        let codebook_sq = codebook_candle.sqr()?.sum(1)?;
        let inner = input_flat.matmul(&codebook_candle.t()?)?;

        let input_sq_expanded = input_sq.unsqueeze(1)?;
        let codebook_sq_expanded = codebook_sq.unsqueeze(0)?;

        let distances = (input_sq_expanded.broadcast_add(&codebook_sq_expanded)? - (inner * 2.0)?)?;

        // Find minimum distance indices
        let indices = distances.argmin(1)?;

        // Gather quantized values from codebook
        let quantized_flat = codebook_candle.index_select(&indices, 0)?;

        // Reshape back: [batch * seq, channels] -> [batch, channels, seq]
        let quantized = quantized_flat.reshape(&[batch, seq_len, channels])?.transpose(1, 2)?;

        let indices_reshaped = indices.reshape(&[batch, seq_len])?;

        Ok((Tensor::from_candle(indices_reshaped), Tensor::from_candle(quantized)))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_encodec_config() {
        let config = EncodecConfig::default();
        assert_eq!(config.hidden_size, 128);
        assert_eq!(config.num_codebooks, 32);
        assert_eq!(config.codebook_size, 1024);
    }

    #[test]
    fn test_encodec_model_creation() {
        let config = EncodecConfig {
            hidden_size: 32,
            num_filters: 8,
            num_residual_layers: 1,
            upsampling_ratios: vec![2, 2],
            num_codebooks: 4,
            codebook_size: 64,
            codebook_dim: 32,
            ..Default::default()
        };

        let model = EncodecModelV2::new(config).unwrap();
        assert_eq!(model.config().hidden_size(), 32);
    }
}