Lots of updates including some stack of command buffers.

2 files changed, 43 insertions, 4 deletions

diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index af4e04b7..e72cab69 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -31,3 +31,4 @@ accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate"]
 cuda = ["candle/cuda", "candle-nn/cuda"]
 flash-attn = ["cuda", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl"]
+metal = ["candle/metal", "candle-nn/metal"]
diff --git a/candle-transformers/src/models/mixformer.rs b/candle-transformers/src/models/mixformer.rs
index e822ca14..c8dae511 100644
--- a/candle-transformers/src/models/mixformer.rs
+++ b/candle-transformers/src/models/mixformer.rs
@@ -142,10 +142,9 @@ impl RotaryEmbedding {
             .to_dtype(DType::F32)?
             .reshape((max_seq_len, 1))?;
         let freqs = t.matmul(&inv_freq)?;
-        Ok(Self {
-            sin: freqs.sin()?,
-            cos: freqs.cos()?,
-        })
+        let sin = freqs.sin()?;
+        let cos = freqs.cos()?;
+        Ok(Self { sin, cos })
     }
 
     fn apply_rotary_emb_qkv(
@@ -273,6 +272,10 @@ impl MHA {
     }
 
     fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
+        let view = xs.to_string();
+        if view.contains("NaN") {
+            panic!("NaN");
+        }
         let _enter = self.span.enter();
         let (b_size, seq_len, _n_embd) = xs.dims3()?;
         let qkv = self
@@ -408,3 +411,38 @@ impl MixFormerSequentialForCausalLM {
         self.blocks.iter_mut().for_each(|b| b.clear_kv_cache())
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test]
+    fn test_rotary() {
+        let dev = Device::new_metal(0).unwrap();
+        for i in 0..10000 {
+            let dim = 8;
+            let max_seq_len = 12;
+            let inv_freq: Vec<_> = (0..dim)
+                .step_by(2)
+                .map(|i| 1f32 / 10000f32.powf(i as f32 / dim as f32))
+                .collect();
+            let inv_freq_len = inv_freq.len();
+            let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), &dev).unwrap();
+            let t = Tensor::arange(0u32, max_seq_len as u32, &dev)
+                .unwrap()
+                .to_dtype(DType::F32)
+                .unwrap()
+                .reshape((max_seq_len, 1))
+                .unwrap();
+            let x: f32 = t.i((1, 0)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 1.0);
+            let x: f32 = inv_freq.i((0, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.1);
+            let freqs = t.matmul(&inv_freq).unwrap();
+            let x: f32 = freqs.i((1, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.1);
+            let sin = freqs.sin().unwrap().contiguous().unwrap();
+            let x: f32 = sin.i((1, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.099833414);
+        }
+    }
+}