add dynamic position encoding to Siglip

candle-examples/examples/siglip/main.rs

@@ -29,6 +29,9 @@ struct Args {
 
     #[arg(long, use_value_delimiter = true)]
     sequences: Option<Vec<String>>,
+
+    #[arg(short, long)]
+    image_size: Option<usize>,
 }
 
 fn load_image<T: AsRef<std::path::Path>>(path: T, image_size: usize) -> anyhow::Result<Tensor> {
@@ -81,7 +84,11 @@ pub fn main() -> anyhow::Result<()> {
             "candle-examples/examples/yolo-v8/assets/bike.jpg".to_string(),
         ],
     };
-    let images = load_images(&vec_imgs, config.vision_config.image_size)?.to_device(&device)?;
+    let images = load_images(
+        &vec_imgs,
+        args.image_size.unwrap_or(config.vision_config.image_size),
+    )?
+    .to_device(&device)?;
     let vb =
         unsafe { VarBuilder::from_mmaped_safetensors(&[model_file.clone()], DType::F32, &device)? };
     let model = siglip::Model::new(&config, vb)?;

candle-transformers/src/models/siglip.rs

@@ -434,8 +434,9 @@ impl Encoder {
 #[derive(Debug, Clone)]
 struct VisionEmbeddings {
     patch_embedding: candle_nn::Conv2d,
-    position_embedding: candle_nn::Embedding,
-    position_ids: Tensor,
+    position_embedding: Tensor,
+    patch_size: usize,
+    base_num_patches_per_side: usize,
 }
 
 impl VisionEmbeddings {
@@ -451,25 +452,52 @@ impl VisionEmbeddings {
             conv2d_cfg,
             vb.pp("patch_embedding"),
         )?;
-        let num_patches = (cfg.image_size / cfg.patch_size).pow(2);
-        let position_ids = Tensor::arange(0, num_patches as i64, vb.device())?;
-        let position_embedding =
-            candle_nn::embedding(num_patches, cfg.hidden_size(), vb.pp("position_embedding"))?;
+        let num_patches_per_side = cfg.image_size / cfg.patch_size;
+        let embedder = candle_nn::embedding(
+            num_patches_per_side.pow(2),
+            cfg.hidden_size(),
+            vb.pp("position_embedding"),
+        )?;
+        let position_embedding = embedder.embeddings();
+        let position_embedding = position_embedding
+            .reshape((
+                1,
+                num_patches_per_side,
+                num_patches_per_side,
+                cfg.hidden_size(),
+            ))?
+            .permute((0, 3, 1, 2))?;
         Ok(Self {
             patch_embedding,
             position_embedding,
-            position_ids,
+            patch_size: cfg.patch_size,
+            base_num_patches_per_side: num_patches_per_side,
         })
     }
 }
 
 impl Module for VisionEmbeddings {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        //embed tokens
         let (_batch, _channels, _height, _width) = xs.dims4()?;
         let embeddings = xs.apply(&self.patch_embedding)?;
-        let embeddings = embeddings.flatten_from(2)?.transpose(1, 2)?;
-        let position_embedding = self.position_embedding.forward(&self.position_ids)?;
-        embeddings.broadcast_add(&position_embedding)
+        // interpolate position embeddings for the current image size (if needed)
+        let num_patches_h = _height / self.patch_size;
+        let num_patches_w = _width / self.patch_size;
+        let resized_position_embedding = if num_patches_w == self.base_num_patches_per_side
+            && num_patches_h == self.base_num_patches_per_side
+        {
+            self.position_embedding.clone()
+        } else {
+            self.position_embedding
+                .interpolate2d(num_patches_h, num_patches_w)?
+        };
+        // Add position embeddings to tokens and flatten from 2D patches to 1D sequence
+        let embeddings = embeddings
+            .broadcast_add(&resized_position_embedding)?
+            .flatten_from(2)?
+            .transpose(1, 2)?;
+        Ok(embeddings)
     }
 }