Only narrow when needed + deactivate the kv cache.

candle-core/examples/llama/main.rs

@@ -24,7 +24,7 @@ mod var_store;
 mod weights;
 
 const CONTEXT_SIZE: usize = 512;
-const USE_KV_CACHE: bool = true;
+const USE_KV_CACHE: bool = false;
 const START_PROMPT: &str = r"
 EDWARD:
 I wonder how our princely father 'scaped,
@@ -268,7 +268,11 @@ impl CausalSelfAttention {
 
     fn apply_rotary_emb(&self, x: &Tensor, freqs_cis: &Tensor) -> Result<Tensor> {
         let mut dims = x.dims().to_vec();
-        let freqs_cis = freqs_cis.narrow(1, freqs_cis.dims()[1] - dims[1], dims[1])?;
+        let freqs_cis = if dims[1] < CONTEXT_SIZE {
+            freqs_cis.narrow(1, CONTEXT_SIZE - dims[1], dims[1])?
+        } else {
+            freqs_cis.clone()
+        };
         let v = dims.pop().unwrap();
         dims.push(v / 2);
         dims.push(2);

candle-core/src/error.rs

@@ -10,6 +10,14 @@ pub enum Error {
         got: DType,
     },
 
+    #[error("invalid args for narrow: {shape:?}, dim: {dim}, start: {start}, len:{len}")]
+    NarrowInvalidArgs {
+        shape: Shape,
+        dim: usize,
+        start: usize,
+        len: usize,
+    },
+
     #[error("{op} only supports contiguous tensors")]
     RequiresContiguous { op: &'static str },
 

candle-core/src/tensor.rs

@@ -349,21 +349,34 @@ impl Tensor {
     }
 
     /// Returns a new tensor that is a narrowed version of the input, the dimension `dim`
-    /// ranges from `start` to `start + length`.
-    pub fn narrow(&self, dim: usize, start: usize, length: usize) -> Result<Self> {
-        let op = if self.track_op() {
-            Some(Op::Narrow(self.clone(), dim, start, length))
+    /// ranges from `start` to `start + len`.
+    pub fn narrow(&self, dim: usize, start: usize, len: usize) -> Result<Self> {
+        let dims = self.dims();
+        if dim >= dims.len() || start + len > dims[dim] {
+            Err(Error::NarrowInvalidArgs {
+                shape: self.shape().clone(),
+                dim,
+                start,
+                len,
+            })?
+        }
+        if start == 0 && dims[dim] == len {
+            Ok(self.clone())
         } else {
-            None
-        };
-        let tensor_ = Tensor_ {
-            id: TensorId::new(),
-            storage: self.storage.clone(),
-            layout: self.layout().narrow(dim, start, length)?,
-            op,
-            is_variable: false,
-        };
-        Ok(Tensor(Arc::new(tensor_)))
+            let op = if self.track_op() {
+                Some(Op::Narrow(self.clone(), dim, start, len))
+            } else {
+                None
+            };
+            let tensor_ = Tensor_ {
+                id: TensorId::new(),
+                storage: self.storage.clone(),
+                layout: self.layout().narrow(dim, start, len)?,
+                op,
+                is_variable: false,
+            };
+            Ok(Tensor(Arc::new(tensor_)))
+        }
     }
 
     pub fn softmax(&self, dim: usize) -> Result<Self> {