Add PaliGemma. (#2519)

* Add PaliGemma.

* PaliGemma inference loop.

* Running PaliGemma example.

* Tweak the prompt.

candle-transformers/src/models/gemma.rs

@@ -362,6 +362,10 @@ impl Model {
         })
     }
 
+    pub fn embed_tokens(&self) -> &candle_nn::Embedding {
+        &self.embed_tokens
+    }
+
     fn prepare_decoder_attention_mask(
         &self,
         b_size: usize,
@@ -400,6 +404,22 @@ impl Model {
             .apply(&self.lm_head)
     }
 
+    pub fn forward_embeds(
+        &mut self,
+        xs: &Tensor,
+        attn_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let (_, seq_len, _) = xs.dims3()?;
+        let mut xs = (xs * (self.hidden_size as f64).sqrt())?;
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attn_mask, seqlen_offset)?
+        }
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm)?
+            .apply(&self.lm_head)
+    }
+
     pub fn clear_kv_cache(&mut self) {
         for layer in self.layers.iter_mut() {
             layer.clear_kv_cache()

candle-transformers/src/models/mod.rs

@@ -46,6 +46,7 @@ pub mod moondream;
 pub mod mpt;
 pub mod olmo;
 pub mod openclip;
+pub mod paligemma;
 pub mod parler_tts;
 pub mod persimmon;
 pub mod phi;

candle-transformers/src/models/paligemma.rs

@@ -0,0 +1,109 @@
+use crate::models::{gemma, siglip};
+use candle::{Module, Result, Tensor};
+use candle_nn::{linear, Linear, VarBuilder};
+
+#[derive(serde::Deserialize, Clone, Debug)]
+pub struct Config {
+    pub vision_config: siglip::VisionConfig,
+    pub text_config: gemma::Config,
+    pub projection_dim: usize,
+}
+
+impl Config {
+    pub fn paligemma_3b_224() -> Self {
+        // https://huggingface.co/google/paligemma-3b-pt-224/blob/main/config.json
+        Self {
+            vision_config: siglip::VisionConfig::paligemma_3b_224(),
+            text_config: gemma::Config {
+                hidden_size: 2048,
+                intermediate_size: 16384,
+                num_attention_heads: 8,
+                num_hidden_layers: 18,
+                num_key_value_heads: 1,
+                vocab_size: 257216,
+                // Default values.
+                rope_theta: 10000.,
+                head_dim: 256,
+                hidden_act: Some(candle_nn::Activation::GeluPytorchTanh),
+                hidden_activation: None,
+                attention_bias: false,
+                max_position_embeddings: 8192,
+                rms_norm_eps: 1e-6,
+            },
+            projection_dim: 2048,
+        }
+    }
+}
+
+#[derive(Clone, Debug)]
+pub struct MultiModalProjector {
+    linear: Linear,
+}
+
+impl MultiModalProjector {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let linear = linear(
+            cfg.vision_config.hidden_size,
+            cfg.projection_dim,
+            vb.pp("linear"),
+        )?;
+        Ok(Self { linear })
+    }
+}
+
+impl Module for MultiModalProjector {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.linear)
+    }
+}
+
+#[derive(Clone, Debug)]
+pub struct Model {
+    pos: usize,
+    vision_tower: siglip::VisionModel,
+    multi_modal_projector: MultiModalProjector,
+    language_model: gemma::Model,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vision_tower = siglip::VisionModel::new(
+            &cfg.vision_config,
+            false,
+            vb.pp("vision_tower.vision_model"),
+        )?;
+        let multi_modal_projector = MultiModalProjector::new(cfg, vb.pp("multi_modal_projector"))?;
+        let language_model = gemma::Model::new(false, &cfg.text_config, vb.pp("language_model"))?;
+        Ok(Self {
+            pos: 0,
+            language_model,
+            vision_tower,
+            multi_modal_projector,
+        })
+    }
+
+    pub fn setup(&mut self, pixel_values: &Tensor, input_ids: &Tensor) -> Result<Tensor> {
+        self.clear_kv_cache();
+        let image_features = self
+            .vision_tower
+            .forward(pixel_values)?
+            .apply(&self.multi_modal_projector)?;
+        let image_features = crate::models::clip::div_l2_norm(&image_features)?;
+        let text_features = self.language_model.embed_tokens().forward(input_ids)?;
+        let input_embeds = Tensor::cat(&[image_features, text_features], 1)?;
+        self.pos = input_embeds.dim(1)?;
+        self.language_model.forward_embeds(&input_embeds, None, 0)
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
+        let pos = self.pos;
+        let seq_len = input_ids.dim(1)?;
+        self.pos = pos + seq_len;
+        self.language_model.forward(input_ids, pos)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.pos = 0;
+        self.language_model.clear_kv_cache()
+    }
+}