candding 0.1.0

Pure candle embeddings for Rust with no ONNX: dense, sparse, multi-vector and rerank models today, image models on the roadmap
Documentation 
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use candle_core::DType;
use candle_core::Module;
use candle_core::Tensor;
use candle_nn::Embedding;
use candle_nn::VarBuilder;
use candle_nn::embedding;

use super::config::JinaBertConfig;
use crate::error::Result;
use crate::layers::layer_norm::LayerNorm;

/// `LayerNorm(word + token_type)`: no position table (ALiBi), but a real token-type table whose row 0 reaches
/// every single-sequence input.
pub struct JinaBertEmbeddings {
  word: Embedding,
  token_type: Embedding,
  layer_norm: LayerNorm,
}

impl JinaBertEmbeddings {
  pub fn load(config: &JinaBertConfig, vb: VarBuilder) -> Result<Self> {
    Ok(Self {
      word: embedding(
        config.vocab_size,
        config.hidden_size,
        vb.pp("word_embeddings"),
      )?,
      token_type: embedding(
        config.type_vocab_size,
        config.hidden_size,
        vb.pp("token_type_embeddings"),
      )?,
      layer_norm: LayerNorm::load(
        vb.pp("LayerNorm"),
        config.hidden_size,
        config.layer_norm_eps,
      )?,
    })
  }

  pub(super) fn weights_dtype(&self) -> DType {
    self.word.embeddings().dtype()
  }

  pub fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
    let summed = (self.word.forward(input_ids)? + self.token_type.forward(token_type_ids)?)?;
    Ok(self.layer_norm.forward(&summed)?)
  }
}