1 files changed, 58 insertions, 40 deletions

diff --git a/candle-examples/examples/whisper/model.rs b/candle-examples/examples/whisper/model.rs
index 4d80c0c8..7015199d 100644
--- a/candle-examples/examples/whisper/model.rs
+++ b/candle-examples/examples/whisper/model.rs
@@ -1,8 +1,5 @@
-// We use anyhow rather than candle errors as it provides better support for getting the backtrace
-// back when using RUST_LIB_BACKTRACE=1.
-use anyhow::Result;
-use candle::{Device, Tensor};
-use candle_nn::{ops::softmax, Conv1d, Conv1dConfig, Embedding, LayerNorm, Linear, VarBuilder};
+use candle::{Device, Result, Tensor};
+use candle_nn::{ops::softmax, Conv1d, Conv1dConfig, Embedding, LayerNorm, VarBuilder};
 use serde::Deserialize;
 
 // The names in comments correspond to the original implementation:
@@ -22,6 +19,7 @@ pub struct Config {
 }
 
 impl Config {
+    #[allow(dead_code)]
     pub fn tiny_en() -> Self {
         Self {
             num_mel_bins: 80,
@@ -42,16 +40,32 @@ fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Em
     let embeddings = vb.get((vocab_size, hidden_size), "weight")?;
     Ok(Embedding::new(embeddings, hidden_size))
 }
+//
+// We wrap the `Linear` layer here to add some tracing so that it's easier to profile the resulting
+// model.
+#[derive(Debug)]
+pub struct Linear {
+    inner: candle_nn::Linear,
+    span: tracing::Span,
+}
+
+impl Linear {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(x)
+    }
+}
 
 fn linear(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
-    let weight = vb.get((size2, size1), "weight")?;
-    let bias = vb.get(size2, "bias")?;
-    Ok(Linear::new(weight, Some(bias)))
+    let span = tracing::span!(tracing::Level::TRACE, "linear");
+    let inner = candle_nn::linear(size1, size2, vb)?;
+    Ok(Linear { inner, span })
 }
 
 fn linear_no_bias(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
-    let weight = vb.get((size2, size1), "weight")?;
-    Ok(Linear::new(weight, None))
+    let span = tracing::span!(tracing::Level::TRACE, "linear");
+    let inner = candle_nn::linear_no_bias(size1, size2, vb)?;
+    Ok(Linear { inner, span })
 }
 
 fn conv1d(
@@ -66,32 +80,6 @@ fn conv1d(
     Ok(Conv1d::new(weight, Some(bias), config))
 }
 
-fn conv1d_no_bias(
-    in_channels: usize,
-    out_channels: usize,
-    kernel_size: usize,
-    config: Conv1dConfig,
-    vb: VarBuilder,
-) -> Result<Conv1d> {
-    let weight = vb.get((out_channels, in_channels, kernel_size), "weight")?;
-    Ok(Conv1d::new(weight, None, config))
-}
-
-struct Dropout {
-    pr: f64,
-}
-
-impl Dropout {
-    fn new(pr: f64) -> Self {
-        Self { pr }
-    }
-
-    fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        // TODO
-        Ok(x.clone())
-    }
-}
-
 fn layer_norm(size: usize, vb: VarBuilder) -> Result<LayerNorm> {
     let weight = vb.get(size, "weight")?;
     let bias = vb.get(size, "bias")?;
@@ -105,10 +93,12 @@ struct MultiHeadAttention {
     value: Linear,
     out: Linear,
     n_head: usize,
+    span: tracing::Span,
 }
 
 impl MultiHeadAttention {
     fn load(n_state: usize, n_head: usize, vb: VarBuilder) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "multi-head-attn");
         let query = linear(n_state, n_state, vb.pp("q_proj"))?;
         let value = linear(n_state, n_state, vb.pp("v_proj"))?;
         let key = linear_no_bias(n_state, n_state, vb.pp("k_proj"))?;
@@ -119,10 +109,12 @@ impl MultiHeadAttention {
             value,
             out,
             n_head,
+            span,
         })
     }
 
     fn forward(&self, x: &Tensor, xa: Option<&Tensor>, mask: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let q = self.query.forward(x)?;
         let k = self.key.forward(xa.unwrap_or(x))?;
         let v = self.value.forward(xa.unwrap_or(x))?;
@@ -134,7 +126,7 @@ impl MultiHeadAttention {
     fn reshape_head(&self, x: &Tensor) -> Result<Tensor> {
         let (n_batch, n_ctx, n_state) = x.dims3()?;
         let target_dims = &[n_batch, n_ctx, self.n_head, n_state / self.n_head];
-        Ok(x.reshape(target_dims)?.transpose(1, 2)?)
+        x.reshape(target_dims)?.transpose(1, 2)
     }
 
     fn qkv_attention(
@@ -168,10 +160,12 @@ struct ResidualAttentionBlock {
     mlp_linear1: Linear,
     mlp_linear2: Linear,
     mlp_ln: LayerNorm,
+    span: tracing::Span,
 }
 
 impl ResidualAttentionBlock {
     fn load(n_state: usize, n_head: usize, ca: bool, vb: VarBuilder) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "residual-attn");
         let attn = MultiHeadAttention::load(n_state, n_head, vb.pp("self_attn"))?;
         let attn_ln = layer_norm(n_state, vb.pp("self_attn_layer_norm"))?;
         let cross_attn = if ca {
@@ -192,10 +186,12 @@ impl ResidualAttentionBlock {
             mlp_linear1,
             mlp_linear2,
             mlp_ln,
+            span,
         })
     }
 
     fn forward(&self, x: &Tensor, xa: Option<&Tensor>, mask: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let attn = self.attn.forward(&self.attn_ln.forward(x)?, None, mask)?;
         let mut x = (x + attn)?;
         if let Some((attn, ln)) = &self.cross_attn {
@@ -207,7 +203,7 @@ impl ResidualAttentionBlock {
                 .forward(&self.mlp_ln.forward(&x)?)?
                 .gelu()?,
         )?;
-        Ok((x + mlp)?)
+        x + mlp
     }
 }
 
@@ -234,10 +230,16 @@ pub struct AudioEncoder {
     positional_embedding: Tensor,
     blocks: Vec<ResidualAttentionBlock>,
     ln_post: LayerNorm,
+    span: tracing::Span,
+    conv1_span: tracing::Span,
+    conv2_span: tracing::Span,
 }
 
 impl AudioEncoder {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "audio-encoder");
+        let conv1_span = tracing::span!(tracing::Level::TRACE, "conv1");
+        let conv2_span = tracing::span!(tracing::Level::TRACE, "conv2");
         let n_state = cfg.d_model;
         let n_head = cfg.encoder_attention_heads;
         let n_ctx = cfg.max_source_positions;
@@ -264,11 +266,22 @@ impl AudioEncoder {
             positional_embedding,
             blocks,
             ln_post,
+            conv1_span,
+            conv2_span,
+            span,
         })
     }
+
     pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let x = self.conv1.forward(x)?.gelu()?;
-        let x = self.conv2.forward(&x)?.gelu()?;
+        let _enter = self.span.enter();
+        let x = {
+            let _enter = self.conv1_span.enter();
+            self.conv1.forward(x)?.gelu()?
+        };
+        let x = {
+            let _enter = self.conv2_span.enter();
+            self.conv2.forward(&x)?.gelu()?
+        };
         let x = x.transpose(1, 2)?;
         let (_bsize, seq_len, _hidden) = x.dims3()?;
         let positional_embedding = self.positional_embedding.narrow(0, 0, seq_len)?;
@@ -288,10 +301,12 @@ pub struct TextDecoder {
     blocks: Vec<ResidualAttentionBlock>,
     ln: LayerNorm,
     mask: Tensor,
+    span: tracing::Span,
 }
 
 impl TextDecoder {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "text-decoder");
         let n_state = cfg.d_model;
         let n_head = cfg.decoder_attention_heads;
         let n_ctx = cfg.max_target_positions;
@@ -314,10 +329,12 @@ impl TextDecoder {
             blocks,
             ln,
             mask,
+            span,
         })
     }
 
     pub fn forward(&self, x: &Tensor, xa: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let x_dims = x.dims();
         let last = x_dims[x_dims.len() - 1];
         let token_embedding = self.token_embedding.forward(x)?;
@@ -354,6 +371,7 @@ impl Whisper {
         })
     }
 
+    #[allow(dead_code)]
     pub fn forward(&self, mel: &Tensor, tokens: &Tensor) -> Result<Tensor> {
         let enc = self.encoder.forward(mel)?;
         let dec = self.decoder.forward(tokens, &enc)?;