diff --git a/candle-transformers/src/models/mixformer.rs b/candle-transformers/src/models/mixformer.rs
index e9451f0e..d9676a35 100644
--- a/candle-transformers/src/models/mixformer.rs
+++ b/candle-transformers/src/models/mixformer.rs
@@ -198,6 +198,7 @@ struct MLP {
     fc1: Linear,
     fc2: Linear,
     act: Activation,
+    span: tracing::Span,
 }
 
 impl MLP {
@@ -209,12 +210,14 @@ impl MLP {
             fc1,
             fc2,
             act: cfg.activation_function,
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
         })
     }
 }
 
 impl Module for MLP {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.fc1)?.apply(&self.act)?.apply(&self.fc2)
     }
 }
@@ -252,6 +255,9 @@ struct MHA {
     n_head: usize,
     softmax_scale: f64,
     span: tracing::Span,
+    span_rope: tracing::Span,
+    span_mask: tracing::Span,
+    span_softmax: tracing::Span,
 }
 
 impl MHA {
@@ -272,6 +278,9 @@ impl MHA {
             rotary_emb,
             softmax_scale,
             span: tracing::span!(tracing::Level::TRACE, "mha"),
+            span_rope: tracing::span!(tracing::Level::TRACE, "rope"),
+            span_mask: tracing::span!(tracing::Level::TRACE, "mask"),
+            span_softmax: tracing::span!(tracing::Level::TRACE, "softmax"),
         })
     }
 
@@ -287,7 +296,10 @@ impl MHA {
             Some((prev_k, _)) => prev_k.dim(1)?,
         };
         // In the python implementation, a single tensor is returned with the third axis of size 3.
-        let (q, k, v) = self.rotary_emb.apply_rotary_emb_qkv(&qkv, seqlen_offset)?;
+        let (q, k, v) = {
+            let _enter = self.span_rope.enter();
+            self.rotary_emb.apply_rotary_emb_qkv(&qkv, seqlen_offset)?
+        };
         let (k, v) = match &self.kv_cache {
             None => (k, v),
             Some((prev_k, prev_v)) => {
@@ -307,13 +319,19 @@ impl MHA {
         // scores = scores + causal_mask.to(dtype=scores.dtype)
         let attn_weights = match mask {
             None => attn_weights,
-            Some(mask) => masked_fill(
-                &attn_weights,
-                &mask.broadcast_left(b_size * self.n_head)?,
-                f32::NEG_INFINITY,
-            )?,
+            Some(mask) => {
+                let _enter = self.span_mask.enter();
+                masked_fill(
+                    &attn_weights,
+                    &mask.broadcast_left(b_size * self.n_head)?,
+                    f32::NEG_INFINITY,
+                )?
+            }
+        };
+        let attn_weights = {
+            let _enter = self.span_softmax.enter();
+            candle_nn::ops::softmax_last_dim(&attn_weights)?
         };
-        let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
 
         // output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
         // attn_weights: b*h,t,s, v: b*h,s,d
diff --git a/candle-transformers/src/models/moondream.rs b/candle-transformers/src/models/moondream.rs
index 717f3bb4..7ad8c921 100644
--- a/candle-transformers/src/models/moondream.rs
+++ b/candle-transformers/src/models/moondream.rs
@@ -1,6 +1,7 @@
 use crate::models::mixformer::{Config as PhiConfig, MixFormerSequentialForCausalLM as PhiModel};
-use candle::{IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, linear_b, Linear, Module, VarBuilder};
+use crate::models::with_tracing::{layer_norm, linear_b, LayerNorm, Linear};
+use candle::{IndexOp, Module, Result, Tensor, D};
+use candle_nn::VarBuilder;
 
 pub struct Config {
     pub phi_config: PhiConfig,
@@ -76,6 +77,7 @@ struct Attention {
     head_dim: usize,
     qkv: Linear,
     proj: Linear,
+    span: tracing::Span,
 }
 
 impl Attention {
@@ -87,12 +89,14 @@ impl Attention {
             head_dim: dim / num_heads,
             qkv,
             proj,
+            span: tracing::span!(tracing::Level::TRACE, "vit-attn"),
         })
     }
 }
 
 impl Module for Attention {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let (b, n, c) = xs.dims3()?;
         let qkv = xs
             .apply(&self.qkv)?
@@ -114,8 +118,9 @@ impl Module for Attention {
 struct VitBlock {
     attn: Attention,
     mlp: Mlp,
-    norm1: candle_nn::LayerNorm,
-    norm2: candle_nn::LayerNorm,
+    norm1: LayerNorm,
+    norm2: LayerNorm,
+    span: tracing::Span,
 }
 
 impl VitBlock {
@@ -129,12 +134,14 @@ impl VitBlock {
             mlp,
             norm1,
             norm2,
+            span: tracing::span!(tracing::Level::TRACE, "vit-block"),
         })
     }
 }
 
 impl Module for VitBlock {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let ys = xs.apply(&self.norm1)?.apply(&self.attn)?;
         let xs = (xs + &ys)?;
         let ys = xs.apply(&self.norm2)?.apply(&self.mlp)?;
@@ -148,7 +155,8 @@ struct VisionTransformer {
     patch_embed: LinearPatchEmbedding,
     pos_embed: Tensor,
     blocks: Vec<VitBlock>,
-    norm: candle_nn::LayerNorm,
+    norm: LayerNorm,
+    span: tracing::Span,
 }
 
 impl VisionTransformer {
@@ -171,12 +179,14 @@ impl VisionTransformer {
             pos_embed,
             blocks,
             norm,
+            span: tracing::span!(tracing::Level::TRACE, "vit"),
         })
     }
 }
 
 impl Module for VisionTransformer {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let mut xs = (&xs.apply(&self.patch_embed)? + &self.pos_embed)?;
         for block in self.blocks.iter() {
             xs = xs.apply(block)?;
@@ -208,6 +218,7 @@ struct Mlp {
     fc1: Linear,
     act: candle_nn::Activation,
     fc2: Linear,
+    span: tracing::Span,
 }
 
 impl Mlp {
@@ -220,12 +231,18 @@ impl Mlp {
     ) -> Result<Self> {
         let fc1 = linear_b(in_features, hidden_features, true, vb.pp("fc1"))?;
         let fc2 = linear_b(hidden_features, out_features, true, vb.pp("fc2"))?;
-        Ok(Self { fc1, act, fc2 })
+        Ok(Self {
+            fc1,
+            act,
+            fc2,
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
     }
 }
 
 impl Module for Mlp {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.fc1)?.apply(&self.act)?.apply(&self.fc2)
     }
 }