feature: support classification head

backends/candle/src/models/distilbert.rs

@@ -3,6 +3,7 @@ use crate::models::Model;
 use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
 use candle_nn::{Embedding, VarBuilder};
 use serde::Deserialize;
+use std::collections::HashMap;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
 #[derive(Debug, Clone, PartialEq, Deserialize)]
@@ -16,6 +17,8 @@ pub struct DistilBertConfig {
     pub max_position_embeddings: usize,
     pub pad_token_id: usize,
     pub model_type: Option<String>,
+    pub classifier_dropout: Option<f64>,
+    pub id2label: Option<HashMap<String, String>>,
 }
 
 #[derive(Debug)]
@@ -318,6 +321,56 @@ impl DistilBertEncoder {
     }
 }
 
+pub trait ClassificationHead {
+    fn forward(&self, hidden_states: &Tensor) -> Result<Tensor>;
+}
+
+pub struct DistilBertClassificationHead {
+    pre_classifier: Linear,
+    classifier: Linear,
+    span: tracing::Span,
+}
+
+impl DistilBertClassificationHead {
+    pub(crate) fn load(vb: VarBuilder, config: &DistilBertConfig) -> Result<Self> {
+        let n_classes = match &config.id2label {
+            None => candle::bail!("`id2label` must be set for classifier models"),
+            Some(id2label) => id2label.len(),
+        };
+
+        let pre_classifier_weight = vb
+            .pp("pre_classifier")
+            .get((config.dim, config.dim), "weight")?;
+        let pre_classifier_bias = vb.pp("pre_classifier").get(config.dim, "bias")?;
+        let pre_classifier = Linear::new(pre_classifier_weight, Some(pre_classifier_bias), None);
+
+        let classifier_weight = vb.pp("classifier").get((n_classes, config.dim), "weight")?;
+        let classifier_bias = vb.pp("classifier").get(n_classes, "bias")?;
+        let classifier = Linear::new(classifier_weight, Some(classifier_bias), None);
+
+        Ok(Self {
+            pre_classifier,
+            classifier,
+            span: tracing::span!(tracing::Level::TRACE, "classifier"),
+        })
+    }
+}
+
+impl ClassificationHead for DistilBertClassificationHead {
+    fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let hidden_states = hidden_states.unsqueeze(1)?;
+
+        let hidden_states = self.pre_classifier.forward(&hidden_states)?;
+        let hidden_states = hidden_states.relu()?;
+
+        let hidden_states = self.classifier.forward(&hidden_states)?;
+        let hidden_states = hidden_states.squeeze(1)?;
+        Ok(hidden_states)
+    }
+}
+
 #[derive(Debug)]
 pub struct DistilBertSpladeHead {
     vocab_transform: Linear,
@@ -368,11 +421,11 @@ impl DistilBertSpladeHead {
     }
 }
 
-#[derive(Debug)]
 pub struct DistilBertModel {
     embeddings: DistilBertEmbeddings,
     encoder: DistilBertEncoder,
     pool: Pool,
+    classifier: Option<Box<dyn ClassificationHead + Send>>,
     splade: Option<DistilBertSpladeHead>,
 
     num_attention_heads: usize,
@@ -385,15 +438,21 @@ pub struct DistilBertModel {
 
 impl DistilBertModel {
     pub fn load(vb: VarBuilder, config: &DistilBertConfig, model_type: ModelType) -> Result<Self> {
-        let pool = match model_type {
+        let (pool, classifier) = match model_type {
+            // Classifier models always use CLS pooling
             ModelType::Classifier => {
-                candle::bail!("`classifier` model type is not supported for DistilBert")
+                let pool = Pool::Cls;
+
+                let classifier: Box<dyn ClassificationHead + Send> =
+                    Box::new(DistilBertClassificationHead::load(vb.clone(), config)?);
+                (pool, Some(classifier))
             }
             ModelType::Embedding(pool) => {
                 if pool == Pool::LastToken {
                     candle::bail!("`last_token` is not supported for DistilBert");
                 }
-                pool
+
+                (pool, None)
             }
         };
 
@@ -424,6 +483,7 @@ impl DistilBertModel {
             embeddings,
             encoder,
             pool,
+            classifier,
             splade,
             num_attention_heads: config.n_heads,
             device: vb.device().clone(),
@@ -660,4 +720,16 @@ impl Model for DistilBertModel {
     fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
         self.forward(batch)
     }
+
+    fn predict(&self, batch: Batch) -> Result<Tensor> {
+        match &self.classifier {
+            None => candle::bail!("`predict` is not implemented for this model"),
+            Some(classifier) => {
+                let (pooled_embeddings, _raw_embeddings) = self.forward(batch)?;
+                let pooled_embeddings =
+                    pooled_embeddings.expect("pooled_embeddings is empty. This is a bug.");
+                classifier.forward(&pooled_embeddings)
+            }
+        }
+    }
 }