candle/candle-examples/examples/musicgen/encodec_model.rs

use crate::nn::conv1d_weight_norm;
use candle::{DType, IndexOp, Module, Result, Tensor};
use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};

// Encodec Model
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py

#[derive(Debug, Clone, PartialEq)]
enum NormType {
    WeightNorm,
    TimeGroupNorm,
    None,
}

#[derive(Debug, Clone, PartialEq)]
pub struct Config {
    target_bandwidths: Vec<f64>,
    sampling_rate: usize,
    audio_channels: usize,
    normalize: bool,
    chunk_length_s: Option<usize>,
    overlap: Option<usize>,
    hidden_size: usize,
    num_filters: usize,
    num_residual_layers: usize,
    upsampling_ratios: Vec<usize>,
    norm_type: NormType,
    kernel_size: usize,
    last_kernel_size: usize,
    residual_kernel_size: usize,
    dilation_growth_rate: usize,
    use_causal_conv: bool,
    pad_mode: &'static str,
    compress: usize,
    num_lstm_layers: usize,
    trim_right_ratio: f64,
    codebook_size: usize,
    codebook_dim: Option<usize>,
    use_conv_shortcut: bool,
}

impl Default for Config {
    fn default() -> Self {
        Self {
            target_bandwidths: vec![1.5, 3.0, 6.0, 12.0, 24.0],
            sampling_rate: 24_000,
            audio_channels: 1,
            normalize: false,
            chunk_length_s: None,
            overlap: None,
            hidden_size: 128,
            num_filters: 32,
            num_residual_layers: 1,
            upsampling_ratios: vec![8, 5, 4, 2],
            norm_type: NormType::WeightNorm,
            kernel_size: 7,
            last_kernel_size: 7,
            residual_kernel_size: 3,
            dilation_growth_rate: 2,
            use_causal_conv: true,
            pad_mode: "reflect",
            compress: 2,
            num_lstm_layers: 2,
            trim_right_ratio: 1.0,
            codebook_size: 1024,
            codebook_dim: None,
            use_conv_shortcut: true,
        }
    }
}

impl Config {
    // https://huggingface.co/facebook/musicgen-small/blob/495da4ad086b3416a27c6187f9239f9fd96f3962/config.json#L6
    pub fn musicgen_small() -> Self {
        Self {
            audio_channels: 1,
            chunk_length_s: None,
            codebook_dim: Some(128),
            codebook_size: 2048,
            compress: 2,
            dilation_growth_rate: 2,
            hidden_size: 128,
            kernel_size: 7,
            last_kernel_size: 7,
            norm_type: NormType::WeightNorm,
            normalize: false,
            num_filters: 64,
            num_lstm_layers: 2,
            num_residual_layers: 1,
            overlap: None,
            pad_mode: "reflect",
            residual_kernel_size: 3,
            sampling_rate: 32_000,
            target_bandwidths: vec![2.2],
            trim_right_ratio: 1.0,
            upsampling_ratios: vec![8, 5, 4, 4],
            use_causal_conv: false,
            use_conv_shortcut: false,
        }
    }

    fn codebook_dim(&self) -> usize {
        self.codebook_dim.unwrap_or(self.codebook_size)
    }

    fn frame_rate(&self) -> usize {
        let hop_length: usize = self.upsampling_ratios.iter().product();
        (self.sampling_rate + hop_length - 1) / hop_length
    }

    fn num_quantizers(&self) -> usize {
        let num = 1000f64
            * self
                .target_bandwidths
                .last()
                .expect("empty target_bandwidths");
        (num as usize) / (self.frame_rate() * 10)
    }
}

// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L340
#[derive(Debug)]
struct EncodecEuclideanCodebook {
    inited: Tensor,
    cluster_size: Tensor,
    embed: Tensor,
    embed_avg: Tensor,
}

impl EncodecEuclideanCodebook {
    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let inited = vb.get(1, "inited")?;
        let cluster_size = vb.get(cfg.codebook_size, "cluster_size")?;
        let e_shape = (cfg.codebook_size, cfg.codebook_dim());
        let embed = vb.get(e_shape, "embed")?;
        let embed_avg = vb.get(e_shape, "embed_avg")?;
        Ok(Self {
            inited,
            cluster_size,
            embed,
            embed_avg,
        })
    }

    fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
        let quantize = self.embed.embedding(embed_ind)?;
        Ok(quantize)
    }
}

#[derive(Debug)]
struct EncodecVectorQuantization {
    codebook: EncodecEuclideanCodebook,
}

impl EncodecVectorQuantization {
    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let codebook = EncodecEuclideanCodebook::load(vb.pp("codebook"), cfg)?;
        Ok(Self { codebook })
    }

    fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
        let quantize = self.codebook.decode(embed_ind)?;
        let quantize = quantize.transpose(1, 2)?;
        Ok(quantize)
    }
}

#[derive(Debug)]
struct EncodecResidualVectorQuantizer {
    layers: Vec<EncodecVectorQuantization>,
}

impl EncodecResidualVectorQuantizer {
    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let vb = &vb.pp("layers");
        let layers = (0..cfg.num_quantizers())
            .map(|i| EncodecVectorQuantization::load(vb.pp(&i.to_string()), cfg))
            .collect::<Result<Vec<_>>>()?;
        Ok(Self { layers })
    }

    fn decode(&self, codes: &Tensor) -> Result<Tensor> {
        let mut quantized_out = Tensor::zeros((), DType::F32, codes.device())?;
        if codes.dim(0)? != self.layers.len() {
            candle::bail!(
                "codes shape {:?} does not match the number of quantization layers {}",
                codes.shape(),
                self.layers.len()
            )
        }
        for (i, layer) in self.layers.iter().enumerate() {
            let quantized = layer.decode(&codes.i(i)?)?;
            quantized_out = quantized.broadcast_add(&quantized_out)?;
        }
        Ok(quantized_out)
    }
}

// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L226
#[derive(Debug)]
struct EncodecLSTM {
    layers: Vec<candle_nn::LSTM>,
}

impl EncodecLSTM {
    fn load(dim: usize, vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let vb = &vb.pp("lstm");
        let mut layers = vec![];
        for layer_idx in 0..cfg.num_lstm_layers {
            let config = candle_nn::LSTMConfig {
                layer_idx,
                ..Default::default()
            };
            let lstm = candle_nn::lstm(dim, dim, config, vb.clone())?;
            layers.push(lstm)
        }
        Ok(Self { layers })
    }
}

impl Module for EncodecLSTM {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        use candle_nn::RNN;
        let mut xs = xs.clone();
        for layer in self.layers.iter() {
            let states = layer.seq(&xs)?;
            xs = layer.states_to_tensor(&states)?;
        }
        Ok(xs)
    }
}

#[derive(Debug)]
struct EncodecConvTranspose1d {
    weight_g: Tensor,
    weight_v: Tensor,
    bias: Tensor,
}

impl EncodecConvTranspose1d {
    fn load(
        in_c: usize,
        out_c: usize,
        k: usize,
        _stride: usize,
        vb: VarBuilder,
        _cfg: &Config,
    ) -> Result<Self> {
        let vb = &vb.pp("conv");
        let weight_g = vb.get((in_c, 1, 1), "weight_g")?;
        let weight_v = vb.get((in_c, out_c, k), "weight_v")?;
        let bias = vb.get(out_c, "bias")?;
        Ok(Self {
            weight_g,
            weight_v,
            bias,
        })
    }
}

impl Module for EncodecConvTranspose1d {
    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
        todo!()
    }
}

#[derive(Debug)]
struct EncodecConv1d {
    causal: bool,
    conv: Conv1d,
    norm: Option<candle_nn::GroupNorm>,
}

impl EncodecConv1d {
    fn load(
        in_c: usize,
        out_c: usize,
        kernel_size: usize,
        stride: usize,
        vb: VarBuilder,
        cfg: &Config,
    ) -> Result<Self> {
        let conv = match cfg.norm_type {
            NormType::WeightNorm => conv1d_weight_norm(
                in_c,
                out_c,
                kernel_size,
                Conv1dConfig {
                    padding: 0,
                    stride,
                    groups: 1,
                    dilation: 1,
                },
                vb.pp("conv"),
            )?,
            NormType::None | NormType::TimeGroupNorm => conv1d(
                in_c,
                out_c,
                kernel_size,
                Conv1dConfig {
                    padding: 0,
                    stride,
                    groups: 1,
                    dilation: 1,
                },
                vb.pp("conv"),
            )?,
        };
        let norm = match cfg.norm_type {
            NormType::None | NormType::WeightNorm => None,
            NormType::TimeGroupNorm => {
                let gn = candle_nn::group_norm(1, out_c, 1e-5, vb.pp("norm"))?;
                Some(gn)
            }
        };
        Ok(Self {
            causal: cfg.use_causal_conv,
            conv,
            norm,
        })
    }
}

impl Module for EncodecConv1d {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        // TODO: padding, depending on causal.
        let xs = self.conv.forward(xs)?;
        match &self.norm {
            None => Ok(xs),
            Some(norm) => xs.apply(norm),
        }
    }
}

#[derive(Debug)]
struct EncodecResnetBlock {
    block_conv1: EncodecConv1d,
    block_conv2: EncodecConv1d,
    shortcut: Option<EncodecConv1d>,
}

impl EncodecResnetBlock {
    fn load(dim: usize, dilations: &[usize], vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let h = dim / cfg.compress;
        let mut layer = Layer::new(vb.pp("block"));
        if dilations.len() != 2 {
            candle::bail!("expected dilations of size 2")
        }
        // TODO: Apply dilations!
        layer.inc();
        let block_conv1 =
            EncodecConv1d::load(dim, h, cfg.residual_kernel_size, 1, layer.next(), cfg)?;
        layer.inc();
        let block_conv2 = EncodecConv1d::load(h, dim, 1, 1, layer.next(), cfg)?;
        let shortcut = if cfg.use_conv_shortcut {
            let conv = EncodecConv1d::load(dim, dim, 1, 1, vb.pp("shortcut"), cfg)?;
            Some(conv)
        } else {
            None
        };
        Ok(Self {
            block_conv1,
            block_conv2,
            shortcut,
        })
    }
}

impl Module for EncodecResnetBlock {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let residual = xs.clone();
        let xs = xs.elu(1.)?;
        let xs = self.block_conv1.forward(&xs)?;
        let xs = xs.elu(1.)?;
        let xs = self.block_conv2.forward(&xs)?;
        let xs = match &self.shortcut {
            None => (xs + residual)?,
            Some(shortcut) => xs.add(&shortcut.forward(&residual)?)?,
        };
        Ok(xs)
    }
}

struct Layer<'a> {
    vb: VarBuilder<'a>,
    cnt: usize,
}

impl<'a> Layer<'a> {
    fn new(vb: VarBuilder<'a>) -> Self {
        Self { vb, cnt: 0 }
    }

    fn inc(&mut self) {
        self.cnt += 1;
    }

    fn next(&mut self) -> VarBuilder {
        let vb = self.vb.pp(&self.cnt.to_string());
        self.cnt += 1;
        vb
    }
}

#[derive(Debug)]
struct EncodecEncoder {
    init_conv: EncodecConv1d,
    sampling_layers: Vec<(Vec<EncodecResnetBlock>, EncodecConv1d)>,
    final_lstm: EncodecLSTM,
    final_conv: EncodecConv1d,
}

impl EncodecEncoder {
    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let mut layer = Layer::new(vb.pp("layers"));
        let init_conv = EncodecConv1d::load(
            cfg.audio_channels,
            cfg.num_filters,
            cfg.kernel_size,
            1,
            layer.next(),
            cfg,
        )?;
        let mut sampling_layers = vec![];
        let mut scaling = 1;
        for &ratio in cfg.upsampling_ratios.iter().rev() {
            let current_scale = scaling * cfg.num_filters;
            let mut resnets = vec![];
            for j in 0..(cfg.num_residual_layers as u32) {
                let resnet = EncodecResnetBlock::load(
                    current_scale,
                    &[cfg.dilation_growth_rate.pow(j), 1],
                    layer.next(),
                    cfg,
                )?;
                resnets.push(resnet)
            }
            layer.inc(); // ELU
            let conv1d = EncodecConv1d::load(
                current_scale,
                current_scale * 2,
                ratio * 2,
                ratio,
                layer.next(),
                cfg,
            )?;
            sampling_layers.push((resnets, conv1d));
            scaling *= 2;
        }
        let final_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
        layer.inc(); // ELU
        let final_conv = EncodecConv1d::load(
            cfg.num_filters * scaling,
            cfg.hidden_size,
            cfg.last_kernel_size,
            1,
            layer.next(),
            cfg,
        )?;
        Ok(Self {
            init_conv,
            sampling_layers,
            final_conv,
            final_lstm,
        })
    }

    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let mut xs = xs.apply(&self.init_conv)?;
        for (resnets, conv) in self.sampling_layers.iter() {
            for resnet in resnets.iter() {
                xs = xs.apply(resnet)?;
            }
            xs = xs.elu(1.0)?.apply(conv)?;
        }
        xs.apply(&self.final_lstm)?
            .elu(1.0)?
            .apply(&self.final_conv)
    }
}

#[derive(Debug)]
struct EncodecDecoder {
    init_conv: EncodecConv1d,
    init_lstm: EncodecLSTM,
    sampling_layers: Vec<(EncodecConvTranspose1d, Vec<EncodecResnetBlock>)>,
    final_conv: EncodecConv1d,
}

impl EncodecDecoder {
    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let mut layer = Layer::new(vb.pp("layers"));
        let mut scaling = usize::pow(2, cfg.upsampling_ratios.len() as u32);
        let init_conv = EncodecConv1d::load(
            cfg.hidden_size,
            cfg.num_filters * scaling,
            cfg.last_kernel_size,
            1,
            layer.next(),
            cfg,
        )?;
        let init_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
        let mut sampling_layers = vec![];
        for &ratio in cfg.upsampling_ratios.iter() {
            let current_scale = scaling * cfg.num_filters;
            layer.inc(); // ELU
            let conv1d = EncodecConvTranspose1d::load(
                current_scale,
                current_scale / 2,
                ratio * 2,
                ratio,
                layer.next(),
                cfg,
            )?;
            let mut resnets = vec![];
            for j in 0..(cfg.num_residual_layers as u32) {
                let resnet = EncodecResnetBlock::load(
                    current_scale / 2,
                    &[cfg.dilation_growth_rate.pow(j), 1],
                    layer.next(),
                    cfg,
                )?;
                resnets.push(resnet)
            }
            sampling_layers.push((conv1d, resnets));
            scaling /= 2;
        }
        layer.inc(); // ELU
        let final_conv = EncodecConv1d::load(
            cfg.num_filters,
            cfg.audio_channels,
            cfg.last_kernel_size,
            1,
            layer.next(),
            cfg,
        )?;
        Ok(Self {
            init_conv,
            init_lstm,
            sampling_layers,
            final_conv,
        })
    }

    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let mut xs = xs.apply(&self.init_conv)?.apply(&self.init_lstm)?;
        for (conv, resnets) in self.sampling_layers.iter() {
            xs = xs.elu(1.)?.apply(conv)?;
            for resnet in resnets.iter() {
                xs = xs.apply(resnet)?
            }
        }
        xs.elu(1.)?.apply(&self.final_conv)
    }
}

#[derive(Debug)]
pub struct EncodecModel {
    encoder: EncodecEncoder,
    decoder: EncodecDecoder,
    quantizer: EncodecResidualVectorQuantizer,
}

impl EncodecModel {
    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
        let encoder = EncodecEncoder::load(vb.pp("encoder"), cfg)?;
        let decoder = EncodecDecoder::load(vb.pp("decoder"), cfg)?;
        let quantizer = EncodecResidualVectorQuantizer::load(vb.pp("quantizer"), cfg)?;
        Ok(Self {
            encoder,
            decoder,
            quantizer,
        })
    }

    pub fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
        todo!()
    }
}