Rename the .r functions to .dims so as to be a bit more explicit. (#220)

1 files changed, 4 insertions, 4 deletions

diff --git a/candle-examples/examples/falcon/model.rs b/candle-examples/examples/falcon/model.rs
index 60821add..bce93c81 100644
--- a/candle-examples/examples/falcon/model.rs
+++ b/candle-examples/examples/falcon/model.rs
@@ -182,7 +182,7 @@ impl FalconRotaryEmbedding {
         key: &Tensor,
         past_kv_len: usize,
     ) -> Result<(Tensor, Tensor)> {
-        let (_batch, seq_len, _head_dim) = query.shape().r3()?;
+        let (_batch, seq_len, _head_dim) = query.dims3()?;
         let (cos, sin) = self.cos_sin(MAX_SEQ_LEN, query.device(), query.dtype())?;
         let cos = cos.narrow(0, past_kv_len, seq_len)?;
         let sin = sin.narrow(0, past_kv_len, seq_len)?;
@@ -245,7 +245,7 @@ impl FalconAttention {
     }
 
     fn split_heads(&self, fused_qkv: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
-        let (b_sz, seq_len, _) = fused_qkv.shape().r3()?;
+        let (b_sz, seq_len, _) = fused_qkv.dims3()?;
         if !self.multi_query {
             let fused_qkv = fused_qkv.reshape((b_sz, seq_len, self.num_heads, 3, self.head_dim))?;
             let q = fused_qkv.narrow(D::Minus2, 0, 1)?.squeeze(D::Minus2)?;
@@ -267,7 +267,7 @@ impl FalconAttention {
         let fused_qkv = self.query_key_value.forward(x)?;
         let head_dim = self.head_dim;
         let (query, key, value) = self.split_heads(&fused_qkv)?;
-        let (b_sz, seq_len, _, _) = query.shape().r4()?;
+        let (b_sz, seq_len, _, _) = query.dims4()?;
         let query = query
             .transpose(1, 2)?
             .reshape((b_sz * self.num_heads, seq_len, head_dim))?;
@@ -465,7 +465,7 @@ impl Falcon {
     }
 
     pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
-        let (b_sz, seq_len) = input_ids.shape().r2()?;
+        let (b_sz, seq_len) = input_ids.dims2()?;
         let mut hidden_state = self.word_embeddings.forward(input_ids)?;
         let past_kv_len = match &self.blocks[0].self_attention.kv_cache {
             Some((k, _)) => k.dim(1)?,