mistralrs_core/embedding/

bert.rs

#![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]

use candle_core::{DType, Device, Result, Tensor};
use candle_nn::{embedding, layer_norm, linear, Embedding, LayerNorm, Linear, Module, VarBuilder};
use hf_hub::{api::sync::ApiBuilder, Repo, RepoType};
use serde::Deserialize;
use tokenizers::Tokenizer;

use crate::{
    engine::BertEmbeddingModel, layers::Activation, utils::log::once_log_info, GLOBAL_HF_CACHE,
};

#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize, Default)]
#[serde(rename_all = "lowercase")]
enum PositionEmbeddingType {
    #[default]
    Absolute,
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/configuration_bert.py#L1
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {
    vocab_size: usize,
    hidden_size: usize,
    num_hidden_layers: usize,
    num_attention_heads: usize,
    intermediate_size: usize,
    pub hidden_act: Activation,
    hidden_dropout_prob: f64,
    max_position_embeddings: usize,
    type_vocab_size: usize,
    initializer_range: f64,
    layer_norm_eps: f64,
    pad_token_id: usize,
    #[serde(default)]
    position_embedding_type: PositionEmbeddingType,
    #[serde(default)]
    use_cache: bool,
    classifier_dropout: Option<f64>,
    model_type: Option<String>,
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L180
struct BertEmbeddings {
    word_embeddings: Embedding,
    position_embeddings: Option<Embedding>,
    token_type_embeddings: Embedding,
    layer_norm: LayerNorm,
    span: tracing::Span,
}

impl BertEmbeddings {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let word_embeddings = embedding(
            config.vocab_size,
            config.hidden_size,
            vb.pp("word_embeddings"),
        )?;
        let position_embeddings = embedding(
            config.max_position_embeddings,
            config.hidden_size,
            vb.pp("position_embeddings"),
        )?;
        let token_type_embeddings = embedding(
            config.type_vocab_size,
            config.hidden_size,
            vb.pp("token_type_embeddings"),
        )?;
        let layer_norm = layer_norm(
            config.hidden_size,
            config.layer_norm_eps,
            vb.pp("LayerNorm"),
        )?;
        Ok(Self {
            word_embeddings,
            position_embeddings: Some(position_embeddings),
            token_type_embeddings,
            layer_norm,
            span: tracing::span!(tracing::Level::TRACE, "embeddings"),
        })
    }

    fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let (_bsize, seq_len) = input_ids.dims2()?;
        let input_embeddings = self.word_embeddings.forward(input_ids)?;
        let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
        let mut embeddings = (&input_embeddings + token_type_embeddings)?;
        if let Some(position_embeddings) = &self.position_embeddings {
            // TODO: Proper absolute positions?
            let position_ids = (0..seq_len as u32).collect::<Vec<_>>();
            let position_ids = Tensor::new(&position_ids[..], input_ids.device())?;
            embeddings = embeddings.broadcast_add(&position_embeddings.forward(&position_ids)?)?
        }
        let embeddings = self.layer_norm.forward(&embeddings)?;
        Ok(embeddings)
    }
}

struct BertSelfAttention {
    query: Linear,
    key: Linear,
    value: Linear,
    num_attention_heads: usize,
    attention_head_size: usize,
    span: tracing::Span,
    span_softmax: tracing::Span,
}

impl BertSelfAttention {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let attention_head_size = config.hidden_size / config.num_attention_heads;
        let all_head_size = config.num_attention_heads * attention_head_size;
        let hidden_size = config.hidden_size;
        let query = linear(hidden_size, all_head_size, vb.pp("query"))?;
        let value = linear(hidden_size, all_head_size, vb.pp("value"))?;
        let key = linear(hidden_size, all_head_size, vb.pp("key"))?;
        Ok(Self {
            query,
            key,
            value,
            num_attention_heads: config.num_attention_heads,
            attention_head_size,
            span: tracing::span!(tracing::Level::TRACE, "self-attn"),
            span_softmax: tracing::span!(tracing::Level::TRACE, "softmax"),
        })
    }

    fn transpose_for_scores(&self, xs: &Tensor) -> Result<Tensor> {
        let mut new_x_shape = xs.dims().to_vec();
        new_x_shape.pop();
        new_x_shape.push(self.num_attention_heads);
        new_x_shape.push(self.attention_head_size);
        let xs = xs.reshape(new_x_shape.as_slice())?.transpose(1, 2)?;
        xs.contiguous()
    }

    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let query_layer = self.query.forward(hidden_states)?;
        let key_layer = self.key.forward(hidden_states)?;
        let value_layer = self.value.forward(hidden_states)?;

        let query_layer = self.transpose_for_scores(&query_layer)?;
        let key_layer = self.transpose_for_scores(&key_layer)?;
        let value_layer = self.transpose_for_scores(&value_layer)?;

        let attention_scores = query_layer.matmul(&key_layer.t()?)?;
        let attention_scores = (attention_scores / (self.attention_head_size as f64).sqrt())?;
        let attention_scores = attention_scores.broadcast_add(attention_mask)?;
        let attention_probs = {
            let _enter_sm = self.span_softmax.enter();
            candle_nn::ops::softmax(&attention_scores, candle_core::D::Minus1)?
        };

        let context_layer = attention_probs.matmul(&value_layer)?;
        let context_layer = context_layer.transpose(1, 2)?.contiguous()?;
        let context_layer = context_layer.flatten_from(candle_core::D::Minus2)?;
        Ok(context_layer)
    }
}

struct BertSelfOutput {
    dense: Linear,
    layer_norm: LayerNorm,
    span: tracing::Span,
}

impl BertSelfOutput {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let dense = linear(config.hidden_size, config.hidden_size, vb.pp("dense"))?;
        let layer_norm = layer_norm(
            config.hidden_size,
            config.layer_norm_eps,
            vb.pp("LayerNorm"),
        )?;
        Ok(Self {
            dense,
            layer_norm,
            span: tracing::span!(tracing::Level::TRACE, "self-out"),
        })
    }

    fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let hidden_states = self.dense.forward(hidden_states)?;
        self.layer_norm.forward(&(hidden_states + input_tensor)?)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L392
struct BertAttention {
    self_attention: BertSelfAttention,
    self_output: BertSelfOutput,
    span: tracing::Span,
}

impl BertAttention {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let self_attention = BertSelfAttention::load(vb.pp("self"), config)?;
        let self_output = BertSelfOutput::load(vb.pp("output"), config)?;
        Ok(Self {
            self_attention,
            self_output,
            span: tracing::span!(tracing::Level::TRACE, "attn"),
        })
    }

    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let self_outputs = self.self_attention.forward(hidden_states, attention_mask)?;
        let attention_output = self.self_output.forward(&self_outputs, hidden_states)?;
        Ok(attention_output)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L441
struct BertIntermediate {
    dense: Linear,
    intermediate_act: Activation,
}

impl BertIntermediate {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let dense = linear(config.hidden_size, config.intermediate_size, vb.pp("dense"))?;
        Ok(Self {
            dense,
            intermediate_act: config.hidden_act,
        })
    }
}

impl Module for BertIntermediate {
    fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
        let hidden_states = self.dense.forward(hidden_states)?;
        let ys = self.intermediate_act.forward(&hidden_states)?;
        Ok(ys)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L456
struct BertOutput {
    dense: Linear,
    layer_norm: LayerNorm,
    span: tracing::Span,
}

impl BertOutput {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let dense = linear(config.intermediate_size, config.hidden_size, vb.pp("dense"))?;
        let layer_norm = layer_norm(
            config.hidden_size,
            config.layer_norm_eps,
            vb.pp("LayerNorm"),
        )?;
        Ok(Self {
            dense,
            layer_norm,
            span: tracing::span!(tracing::Level::TRACE, "out"),
        })
    }

    fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let hidden_states = self.dense.forward(hidden_states)?;
        self.layer_norm.forward(&(hidden_states + input_tensor)?)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L470
struct BertLayer {
    attention: BertAttention,
    intermediate: BertIntermediate,
    output: BertOutput,
    span: tracing::Span,
}

impl BertLayer {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let attention = BertAttention::load(vb.pp("attention"), config)?;
        let intermediate = BertIntermediate::load(vb.pp("intermediate"), config)?;
        let output = BertOutput::load(vb.pp("output"), config)?;
        Ok(Self {
            attention,
            intermediate,
            output,
            span: tracing::span!(tracing::Level::TRACE, "layer"),
        })
    }

    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let attention_output = self.attention.forward(hidden_states, attention_mask)?;
        // TODO: Support cross-attention?
        // https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L523
        // TODO: Support something similar to `apply_chunking_to_forward`?
        let intermediate_output = self.intermediate.forward(&attention_output)?;
        let layer_output = self
            .output
            .forward(&intermediate_output, &attention_output)?;
        Ok(layer_output)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L556
struct BertEncoder {
    layers: Vec<BertLayer>,
    span: tracing::Span,
}

impl BertEncoder {
    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let layers = (0..config.num_hidden_layers)
            .map(|index| BertLayer::load(vb.pp(format!("layer.{index}")), config))
            .collect::<Result<Vec<_>>>()?;
        let span = tracing::span!(tracing::Level::TRACE, "encoder");
        Ok(BertEncoder { layers, span })
    }

    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
        let _enter = self.span.enter();
        let mut hidden_states = hidden_states.clone();
        // Use a loop rather than a fold as it's easier to modify when adding debug/...
        for layer in self.layers.iter() {
            hidden_states = layer.forward(&hidden_states, attention_mask)?
        }
        Ok(hidden_states)
    }
}

// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L874
pub struct BertModel {
    embeddings: BertEmbeddings,
    encoder: BertEncoder,
    span: tracing::Span,
}

impl BertModel {
    pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
        let (embeddings, encoder) = match (
            BertEmbeddings::load(vb.pp("embeddings"), config),
            BertEncoder::load(vb.pp("encoder"), config),
        ) {
            (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
            (Err(err), _) | (_, Err(err)) => {
                if let Some(model_type) = &config.model_type {
                    if let (Ok(embeddings), Ok(encoder)) = (
                        BertEmbeddings::load(vb.pp(format!("{model_type}.embeddings")), config),
                        BertEncoder::load(vb.pp(format!("{model_type}.encoder")), config),
                    ) {
                        (embeddings, encoder)
                    } else {
                        return Err(err);
                    }
                } else {
                    return Err(err);
                }
            }
        };
        Ok(Self {
            embeddings,
            encoder,
            span: tracing::span!(tracing::Level::TRACE, "model"),
        })
    }

    fn get_extended_attention_mask(attention_mask: &Tensor, dtype: DType) -> Result<Tensor> {
        let attention_mask = match attention_mask.rank() {
            3 => attention_mask.unsqueeze(1)?,
            2 => attention_mask.unsqueeze(1)?.unsqueeze(1)?,
            _ => candle_core::bail!("Wrong shape for input_ids or attention_mask"),
        };
        let attention_mask = attention_mask.to_dtype(dtype)?;
        // torch.finfo(dtype).min
        (attention_mask.ones_like()? - &attention_mask)?.broadcast_mul(
            &Tensor::try_from(f32::MIN)?
                .to_dtype(dtype)?
                .to_device(attention_mask.device())?,
        )
    }

    pub fn forward(
        &self,
        input_ids: &Tensor,
        token_type_ids: &Tensor,
        attention_mask: Option<&Tensor>,
    ) -> Result<Tensor> {
        let _enter = self.span.enter();
        let embedding_output = self.embeddings.forward(input_ids, token_type_ids)?;
        let attention_mask = match attention_mask {
            Some(attention_mask) => attention_mask.clone(),
            None => input_ids.ones_like()?,
        };
        // https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L995
        let attention_mask =
            Self::get_extended_attention_mask(&attention_mask, embedding_output.dtype())?;
        let sequence_output = self.encoder.forward(&embedding_output, &attention_mask)?;
        Ok(sequence_output)
    }
}

pub struct BertPipeline {
    pub model: BertModel,
    pub tokenizer: Tokenizer,
}

impl BertPipeline {
    pub fn new(model: BertEmbeddingModel, device: &Device) -> anyhow::Result<Self> {
        let model_id = match model {
            BertEmbeddingModel::SnowflakeArcticEmbedL => {
                "Snowflake/snowflake-arctic-embed-l-v2.0".to_string()
            }
            BertEmbeddingModel::Custom(model_id) => model_id,
        };
        once_log_info(format!("Loading embedding model ({model_id})."));

        let repo = Repo::with_revision(model_id, RepoType::Model, "main".to_string());
        let (config_filename, tokenizer_filename, weights_filename) = {
            let cache = GLOBAL_HF_CACHE.get().cloned().unwrap_or_default();
            let api = ApiBuilder::from_cache(cache)
                .with_progress(true)
                .with_token(None)
                .build()?;
            let api = api.repo(repo);
            let config = api.get("config.json")?;
            let tokenizer = api.get("tokenizer.json")?;
            let weights = api.get("model.safetensors")?;
            (config, tokenizer, weights)
        };
        let config = std::fs::read_to_string(config_filename)?;
        let config: Config = serde_json::from_str(&config)?;
        let tokenizer =
            Tokenizer::from_file(tokenizer_filename).map_err(candle_core::Error::msg)?;

        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&[weights_filename], DType::F32, device)?
        };
        let model = BertModel::load(vb, &config)?;
        Ok(Self { model, tokenizer })
    }
}