candle/candle-examples/examples/stable-diffusion/clip.rs

//! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! https://github.com/openai/CLIP
use candle::{DType, Device, Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;

#[derive(Debug, Clone, Copy)]
pub enum Activation {
    QuickGelu,
    Gelu,
}

impl Activation {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        match self {
            Activation::QuickGelu => xs * nn::ops::sigmoid(&(xs * 1.702f64)?)?,
            Activation::Gelu => xs.gelu(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct Config {
    vocab_size: usize,
    embed_dim: usize,       // aka config.hidden_size
    activation: Activation, // aka config.hidden_act
    intermediate_size: usize,
    pub max_position_embeddings: usize,
    // The character to use for padding, use EOS when not set.
    pub pad_with: Option<String>,
    num_hidden_layers: usize,
    num_attention_heads: usize,
    #[allow(dead_code)]
    projection_dim: usize,
}

impl Config {
    // The config details can be found in the "text_config" section of this json file:
    // https://huggingface.co/openai/clip-vit-large-patch14/blob/main/config.json
    pub fn v1_5() -> Self {
        Self {
            vocab_size: 49408,
            embed_dim: 768,
            intermediate_size: 3072,
            max_position_embeddings: 77,
            pad_with: None,
            num_hidden_layers: 12,
            num_attention_heads: 12,
            projection_dim: 768,
            activation: Activation::QuickGelu,
        }
    }

    // https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/text_encoder/config.json
    pub fn v2_1() -> Self {
        Self {
            vocab_size: 49408,
            embed_dim: 1024,
            intermediate_size: 4096,
            max_position_embeddings: 77,
            pad_with: Some("!".to_string()),
            num_hidden_layers: 23,
            num_attention_heads: 16,
            projection_dim: 512,
            activation: Activation::Gelu,
        }
    }
}

// CLIP Text Model
// https://github.com/huggingface/transformers/blob/674f750a57431222fa2832503a108df3badf1564/src/transformers/models/clip/modeling_clip.py
#[derive(Debug)]
struct ClipTextEmbeddings {
    token_embedding: candle_nn::Embedding,
    position_embedding: candle_nn::Embedding,
    position_ids: Tensor,
}

impl ClipTextEmbeddings {
    fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let token_embedding =
            candle_nn::embedding(c.vocab_size, c.embed_dim, vs.pp("token_embedding"))?;
        let position_embedding = candle_nn::embedding(
            c.max_position_embeddings,
            c.embed_dim,
            vs.pp("position_embedding"),
        )?;
        let position_ids =
            Tensor::arange(0u32, c.max_position_embeddings as u32, vs.device())?.unsqueeze(0)?;
        Ok(ClipTextEmbeddings {
            token_embedding,
            position_embedding,
            position_ids,
        })
    }
}

impl ClipTextEmbeddings {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let token_embedding = self.token_embedding.forward(xs)?;
        let position_embedding = self.position_embedding.forward(&self.position_ids)?;
        token_embedding.broadcast_add(&position_embedding)
    }
}

#[derive(Debug)]
struct ClipAttention {
    k_proj: candle_nn::Linear,
    v_proj: candle_nn::Linear,
    q_proj: candle_nn::Linear,
    out_proj: candle_nn::Linear,
    head_dim: usize,
    scale: f64,
    num_attention_heads: usize,
}

impl ClipAttention {
    fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let embed_dim = c.embed_dim;
        let num_attention_heads = c.num_attention_heads;
        let k_proj = candle_nn::linear(embed_dim, embed_dim, vs.pp("k_proj"))?;
        let v_proj = candle_nn::linear(embed_dim, embed_dim, vs.pp("v_proj"))?;
        let q_proj = candle_nn::linear(embed_dim, embed_dim, vs.pp("q_proj"))?;
        let out_proj = candle_nn::linear(embed_dim, embed_dim, vs.pp("out_proj"))?;
        let head_dim = embed_dim / num_attention_heads;
        let scale = (head_dim as f64).powf(-0.5);
        Ok(ClipAttention {
            k_proj,
            v_proj,
            q_proj,
            out_proj,
            head_dim,
            scale,
            num_attention_heads,
        })
    }

    fn shape(&self, xs: &Tensor, seq_len: usize, bsz: usize) -> Result<Tensor> {
        xs.reshape((bsz, seq_len, self.num_attention_heads, self.head_dim))?
            .transpose(1, 2)?
            .contiguous()
    }

    fn forward(&self, xs: &Tensor, causal_attention_mask: &Tensor) -> Result<Tensor> {
        let in_dtype = xs.dtype();
        let (bsz, seq_len, embed_dim) = xs.dims3()?;
        let query_states = (self.q_proj.forward(xs)? * self.scale)?;
        let proj_shape = (bsz * self.num_attention_heads, seq_len, self.head_dim);
        let query_states = self
            .shape(&query_states, seq_len, bsz)?
            .reshape(proj_shape)?
            .to_dtype(DType::F32)?;
        let key_states = self
            .shape(&self.k_proj.forward(xs)?, seq_len, bsz)?
            .reshape(proj_shape)?
            .to_dtype(DType::F32)?;
        let value_states = self
            .shape(&self.v_proj.forward(xs)?, seq_len, bsz)?
            .reshape(proj_shape)?
            .to_dtype(DType::F32)?;
        let attn_weights = query_states.matmul(&key_states.transpose(1, 2)?)?;

        let src_len = key_states.dim(1)?;
        let attn_weights = attn_weights
            .reshape((bsz, self.num_attention_heads, seq_len, src_len))?
            .broadcast_add(causal_attention_mask)?;
        let attn_weights =
            attn_weights.reshape((bsz * self.num_attention_heads, seq_len, src_len))?;
        let attn_weights = candle_nn::ops::softmax(&attn_weights, D::Minus1)?;

        let attn_output = attn_weights.matmul(&value_states)?.to_dtype(in_dtype)?;
        let attn_output = attn_output
            .reshape((bsz, self.num_attention_heads, seq_len, self.head_dim))?
            .transpose(1, 2)?
            .reshape((bsz, seq_len, embed_dim))?;
        self.out_proj.forward(&attn_output)
    }
}

#[derive(Debug)]
struct ClipMlp {
    fc1: candle_nn::Linear,
    fc2: candle_nn::Linear,
    activation: Activation,
}

impl ClipMlp {
    fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let fc1 = candle_nn::linear(c.embed_dim, c.intermediate_size, vs.pp("fc1"))?;
        let fc2 = candle_nn::linear(c.intermediate_size, c.embed_dim, vs.pp("fc2"))?;
        Ok(ClipMlp {
            fc1,
            fc2,
            activation: c.activation,
        })
    }
}

impl ClipMlp {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let xs = self.fc1.forward(xs)?;
        self.fc2.forward(&self.activation.forward(&xs)?)
    }
}

#[derive(Debug)]
struct ClipEncoderLayer {
    self_attn: ClipAttention,
    layer_norm1: candle_nn::LayerNorm,
    mlp: ClipMlp,
    layer_norm2: candle_nn::LayerNorm,
}

impl ClipEncoderLayer {
    fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let self_attn = ClipAttention::new(vs.pp("self_attn"), c)?;
        let layer_norm1 = candle_nn::layer_norm(c.embed_dim, 1e-5, vs.pp("layer_norm1"))?;
        let mlp = ClipMlp::new(vs.pp("mlp"), c)?;
        let layer_norm2 = candle_nn::layer_norm(c.embed_dim, 1e-5, vs.pp("layer_norm2"))?;
        Ok(ClipEncoderLayer {
            self_attn,
            layer_norm1,
            mlp,
            layer_norm2,
        })
    }

    fn forward(&self, xs: &Tensor, causal_attention_mask: &Tensor) -> Result<Tensor> {
        let residual = xs;
        let xs = self.layer_norm1.forward(xs)?;
        let xs = self.self_attn.forward(&xs, causal_attention_mask)?;
        let xs = (xs + residual)?;

        let residual = &xs;
        let xs = self.layer_norm2.forward(&xs)?;
        let xs = self.mlp.forward(&xs)?;
        xs + residual
    }
}

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

impl ClipEncoder {
    fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let vs = vs.pp("layers");
        let mut layers: Vec<ClipEncoderLayer> = Vec::new();
        for index in 0..c.num_hidden_layers {
            let layer = ClipEncoderLayer::new(vs.pp(&index.to_string()), c)?;
            layers.push(layer)
        }
        Ok(ClipEncoder { layers })
    }

    fn forward(&self, xs: &Tensor, causal_attention_mask: &Tensor) -> Result<Tensor> {
        let mut xs = xs.clone();
        for layer in self.layers.iter() {
            xs = layer.forward(&xs, causal_attention_mask)?;
        }
        Ok(xs)
    }
}

/// A CLIP transformer based model.
#[derive(Debug)]
pub struct ClipTextTransformer {
    embeddings: ClipTextEmbeddings,
    encoder: ClipEncoder,
    final_layer_norm: candle_nn::LayerNorm,
}

impl ClipTextTransformer {
    pub fn new(vs: candle_nn::VarBuilder, c: &Config) -> Result<Self> {
        let vs = vs.pp("text_model");
        let embeddings = ClipTextEmbeddings::new(vs.pp("embeddings"), c)?;
        let encoder = ClipEncoder::new(vs.pp("encoder"), c)?;
        let final_layer_norm = candle_nn::layer_norm(c.embed_dim, 1e-5, vs.pp("final_layer_norm"))?;
        Ok(ClipTextTransformer {
            embeddings,
            encoder,
            final_layer_norm,
        })
    }

    // https://github.com/huggingface/transformers/blob/674f750a57431222fa2832503a108df3badf1564/src/transformers/models/clip/modeling_clip.py#L678
    fn build_causal_attention_mask(bsz: usize, seq_len: usize, device: &Device) -> Result<Tensor> {
        let mask: Vec<_> = (0..seq_len)
            .flat_map(|i| (0..seq_len).map(move |j| if j > i { f32::MIN } else { 0. }))
            .collect();
        let mask = Tensor::from_slice(&mask, (seq_len, seq_len), device)?;
        mask.broadcast_as((bsz, seq_len, seq_len))
    }
}

impl ClipTextTransformer {
    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let (bsz, seq_len) = xs.dims2()?;
        let xs = self.embeddings.forward(xs)?;
        let causal_attention_mask = Self::build_causal_attention_mask(bsz, seq_len, xs.device())?;
        let xs = self.encoder.forward(&xs, &causal_attention_mask)?;
        self.final_layer_norm.forward(&xs)
    }
}