Add the conv1d layer (but not the op).

candle-examples/examples/whisper/main.rs

@@ -172,6 +172,79 @@ impl Linear {
     }
 }
 
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+struct ConvConfig {
+    padding: usize,
+    stride: usize,
+}
+
+impl Default for ConvConfig {
+    fn default() -> Self {
+        Self {
+            padding: 0,
+            stride: 1,
+        }
+    }
+}
+
+struct Conv1D {
+    weight: Tensor,
+    bias: Option<Tensor>,
+    config: ConvConfig,
+}
+
+impl Conv1D {
+    fn load(
+        in_channels: usize,
+        out_channels: usize,
+        kernel_size: usize,
+        config: ConvConfig,
+        p: &str,
+        vb: &VarBuilder,
+    ) -> Result<Self> {
+        let weight = vb.get(
+            (out_channels, in_channels, kernel_size),
+            &format!("{p}.weight"),
+        )?;
+        let bias = vb.get(out_channels, &format!("{p}.bias"))?;
+        Ok(Self {
+            weight,
+            bias: Some(bias),
+            config,
+        })
+    }
+
+    fn load_no_bias(
+        in_channels: usize,
+        out_channels: usize,
+        kernel_size: usize,
+        config: ConvConfig,
+        p: &str,
+        vb: &VarBuilder,
+    ) -> Result<Self> {
+        let weight = vb.get(
+            (out_channels, in_channels, kernel_size),
+            &format!("{p}.weight"),
+        )?;
+        Ok(Self {
+            weight,
+            bias: None,
+            config,
+        })
+    }
+
+    fn forward(&self, x: &Tensor) -> candle::Result<Tensor> {
+        let (bsize, _, _) = x.shape().r3()?;
+        let w = self.weight.broadcast_left(bsize)?.t()?;
+        // TODO: Add the conv1d operation
+        let x = x.matmul(&w)?;
+        match &self.bias {
+            None => Ok(x),
+            Some(bias) => x.broadcast_add(bias),
+        }
+    }
+}
+
 struct Dropout {
     pr: f64,
 }
@@ -341,8 +414,8 @@ fn sinusoids(length: usize, channels: usize) -> Result<Tensor> {
 
 // https://github.com/openai/whisper/blob/f572f2161ba831bae131364c3bffdead7af6d210/whisper/model.py#L143
 struct AudioEncoder {
-    conv1: Linear, // TODO
-    conv2: Linear, // TODO
+    conv1: Conv1D,
+    conv2: Conv1D,
     positional_embedding: Tensor,
     blocks: Vec<ResidualAttentionBlock>,
     ln_post: LayerNorm,
@@ -352,8 +425,16 @@ impl AudioEncoder {
     fn load(p: &str, vb: &VarBuilder, cfg: &Config) -> Result<Self> {
         let n_state = cfg.n_audio_state;
         let n_head = cfg.n_audio_head;
-        let conv1 = Linear::load(cfg.n_mels, n_state, &format!("{p}.conv1"), vb)?;
-        let conv2 = Linear::load(n_state, n_state, &format!("{p}.conv2"), vb)?;
+        let cfg1 = ConvConfig {
+            padding: 1,
+            stride: 1,
+        };
+        let cfg2 = ConvConfig {
+            padding: 1,
+            stride: 2,
+        };
+        let conv1 = Conv1D::load(cfg.n_mels, n_state, 3, cfg1, &format!("{p}.conv1"), vb)?;
+        let conv2 = Conv1D::load(n_state, n_state, 3, cfg2, &format!("{p}.conv2"), vb)?;
         let positional_embedding = sinusoids(cfg.n_audio_ctx, n_state)?.to_device(&vb.device)?;
         let blocks = (0..cfg.n_audio_layer)
             .map(|i| {