Add options to use local files + specify a custom repo or branch. (#1973)

candle-examples/examples/moondream/main.rs

@@ -155,6 +155,18 @@ struct Args {
     /// The context size to consider for the repeat penalty.
     #[arg(long, default_value_t = 64)]
     repeat_last_n: usize,
+
+    #[arg(long, default_value = "vikhyatk/moondream2")]
+    model_id: String,
+
+    #[arg(long, default_value = "main")]
+    revision: String,
+
+    #[arg(long)]
+    model_file: Option<String>,
+
+    #[arg(long)]
+    tokenizer_file: Option<String>,
 }
 
 /// Loads an image from disk using the image crate, this returns a tensor with shape
@@ -204,9 +216,19 @@ async fn main() -> anyhow::Result<()> {
 
     let start = std::time::Instant::now();
     let api = hf_hub::api::tokio::Api::new()?;
-    let repo = api.model("vikhyatk/moondream2".to_string());
-    let model_file = repo.get("model.safetensors").await?;
-    let tokenizer = repo.get("tokenizer.json").await?;
+    let repo = api.repo(hf_hub::Repo::with_revision(
+        args.model_id,
+        hf_hub::RepoType::Model,
+        args.revision,
+    ));
+    let model_file = match args.model_file {
+        Some(m) => m.into(),
+        None => repo.get("model.safetensors").await?,
+    };
+    let tokenizer = match args.tokenizer_file {
+        Some(m) => m.into(),
+        None => repo.get("tokenizer.json").await?,
+    };
     println!("retrieved the files in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
 

candle-transformers/src/models/moondream.rs

@@ -19,11 +19,8 @@ impl Config {
 fn scaled_dot_product_attention(q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
     let dim = q.dim(D::Minus1)?;
     let scale_factor = 1.0 / (dim as f64).sqrt();
-    let k = k.transpose(D::Minus2, D::Minus1)?.contiguous()?;
-    let mut attn_weights = (q.contiguous()?.matmul(&k)? * scale_factor)?;
-    attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?.contiguous()?;
-    let attn_weights = attn_weights.matmul(&v.contiguous()?)?;
-    Ok(attn_weights)
+    let attn_weights = (q.matmul(&k.t()?)? * scale_factor)?;
+    candle_nn::ops::softmax_last_dim(&attn_weights)?.matmul(v)
 }
 
 #[derive(Debug, Clone, PartialEq, serde::Deserialize)]
@@ -101,10 +98,15 @@ impl Module for Attention {
             .apply(&self.qkv)?
             .reshape((b, n, 3, self.num_heads, self.head_dim))?
             .permute((2, 0, 3, 1, 4))?;
-        let (q, k, v) = (qkv.i(0)?, qkv.i(1)?, qkv.i(2)?);
-        let attn_weights = scaled_dot_product_attention(&q, &k, &v)?;
-        let attn_weights = attn_weights.transpose(1, 2)?.reshape((b, n, c))?;
-        attn_weights.apply(&self.proj)
+        let (q, k, v) = (
+            qkv.i(0)?.contiguous()?,
+            qkv.i(1)?.contiguous()?,
+            qkv.i(2)?.contiguous()?,
+        );
+        scaled_dot_product_attention(&q, &k, &v)?
+            .transpose(1, 2)?
+            .reshape((b, n, c))?
+            .apply(&self.proj)
     }
 }
 
@@ -275,11 +277,11 @@ impl Module for VisionEncoder {
         let (p1, p2) = (14, 14);
         let h = hp1 / p1;
         let w = wp2 / p2;
-        let xs = xs
-            .reshape((b, c, h, p1, h, p2))?
+        xs.reshape((b, c, h, p1, h, p2))?
             .permute((0, 2, 4, 1, 3, 5))?
-            .reshape((b, h * w, c * p1 * p2))?;
-        xs.apply(&self.encoder)?.apply(&self.projection)
+            .reshape((b, h * w, c * p1 * p2))?
+            .apply(&self.encoder)?
+            .apply(&self.projection)
     }
 }