Use faster rotary embeddings for llama like models. (#2087)

1 files changed, 6 insertions, 11 deletions

diff --git a/candle-transformers/src/models/llama.rs b/candle-transformers/src/models/llama.rs
index 97a40d37..945c0e17 100644
--- a/candle-transformers/src/models/llama.rs
+++ b/candle-transformers/src/models/llama.rs
@@ -116,7 +116,6 @@ impl Cache {
             .matmul(&theta.reshape((1, theta.elem_count()))?)?;
         // This is different from the paper, see:
         // https://github.com/huggingface/transformers/blob/6112b1c6442aaf7affd2b0676a1cd4eee30c45cf/src/transformers/models/llama/modeling_llama.py#L112
-        let idx_theta = Tensor::cat(&[&idx_theta, &idx_theta], D::Minus1)?;
         let cos = idx_theta.cos()?.to_dtype(dtype)?;
         let sin = idx_theta.sin()?.to_dtype(dtype)?;
         Ok(Self {
@@ -176,16 +175,10 @@ fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Ten
 impl CausalSelfAttention {
     fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize, cache: &Cache) -> Result<Tensor> {
         let _enter = self.span_rot.enter();
-        let (b_sz, _, seq_len, hidden_size) = x.dims4()?;
+        let (_b_sz, _, seq_len, _hidden_size) = x.dims4()?;
         let cos = cache.cos.narrow(0, index_pos, seq_len)?;
         let sin = cache.sin.narrow(0, index_pos, seq_len)?;
-        let cos = cos.broadcast_as((b_sz, 1, seq_len, hidden_size))?;
-        let sin = sin.broadcast_as((b_sz, 1, seq_len, hidden_size))?;
-        let x1 = x.narrow(D::Minus1, 0, hidden_size / 2)?;
-        let x2 = x.narrow(D::Minus1, hidden_size / 2, hidden_size / 2)?;
-        let rotate_x = Tensor::cat(&[&x2.neg()?, &x1], D::Minus1)?;
-        let rope = (x.broadcast_mul(&cos)? + rotate_x.broadcast_mul(&sin)?)?;
-        Ok(rope)
+        candle_nn::rotary_emb::rope(x, &cos, &sin)
     }
 
     fn forward(
@@ -203,10 +196,12 @@ impl CausalSelfAttention {
 
         let q = q
             .reshape((b_sz, seq_len, self.num_attention_heads, self.head_dim))?
-            .transpose(1, 2)?;
+            .transpose(1, 2)?
+            .contiguous()?;
         let k = k
             .reshape((b_sz, seq_len, self.num_key_value_heads, self.head_dim))?
-            .transpose(1, 2)?;
+            .transpose(1, 2)?
+            .contiguous()?;
         let mut v = v
             .reshape((b_sz, seq_len, self.num_key_value_heads, self.head_dim))?
             .transpose(1, 2)?;