Simplify the KvCache api. (#2207)

candle-examples/examples/quantized-phi/main.rs

@@ -217,7 +217,6 @@ fn main() -> anyhow::Result<()> {
         match args.which {
             Which::Phi2 => Model::Phi2(Phi2::from_gguf(model, &mut file, &device)?),
             Which::Phi3 => Model::Phi3(Phi3::from_gguf(
-                1,
                 args.use_flash_attn,
                 model,
                 &mut file,

candle-nn/src/kv_cache.rs

@@ -1,30 +1,25 @@
-use candle::{DType, Device, Result, Shape, Tensor};
+use candle::{Result, Tensor};
 
 #[derive(Debug, Clone)]
 pub struct Cache {
-    all_data: Tensor,
+    // all_data is an option on a Tensor, this makes it possible to only create the actual tensor
+    // on the first call where the batch size is easily known.
+    // Also this makes it safe to clone a KvCache that has been reseted (as in it will not share
+    // its internal state with the cloned instance).
+    all_data: Option<Tensor>,
     dim: usize,
     current_seq_len: usize,
     max_seq_len: usize,
 }
 
 impl Cache {
-    pub fn new<S: Into<Shape>, D: candle::shape::Dim>(
-        dim: D,
-        shape: S,
-        dtype: DType,
-        dev: &Device,
-    ) -> Result<Self> {
-        let shape = shape.into();
-        let dim = dim.to_index(&shape, "kv-cache")?;
-        let max_seq_len = shape.dims()[dim];
-        let all_data = Tensor::zeros(shape, dtype, dev)?;
-        Ok(Self {
-            all_data,
+    pub fn new(dim: usize, max_seq_len: usize) -> Self {
+        Self {
+            all_data: None,
             dim,
             current_seq_len: 0,
             max_seq_len,
-        })
+        }
     }
 
     pub fn dim(&self) -> usize {
@@ -39,20 +34,34 @@ impl Cache {
         self.max_seq_len
     }
 
-    pub fn all_data(&self) -> &Tensor {
+    pub fn all_data(&self) -> &Option<Tensor> {
         &self.all_data
     }
 
-    pub fn current_data(&self) -> Result<Tensor> {
-        self.all_data.narrow(self.dim, 0, self.current_seq_len)
+    pub fn current_data(&self) -> Result<Option<Tensor>> {
+        let data = match self.all_data.as_ref() {
+            None => None,
+            Some(d) => Some(d.narrow(self.dim, 0, self.current_seq_len)?),
+        };
+        Ok(data)
     }
 
     pub fn reset(&mut self) {
-        self.current_seq_len = 0
+        self.current_seq_len = 0;
+        self.all_data = None;
     }
 
     pub fn append(&mut self, src: &Tensor) -> Result<()> {
         let seq_len = src.dim(self.dim)?;
+        // This doesn't seem very idiomatic but because the creation can fail, it's tricky to use
+        // self.all_data.get_or_insert_with.
+        if self.all_data.is_none() {
+            let mut shape = src.dims().to_vec();
+            shape[self.dim] = self.max_seq_len;
+            let ad = Tensor::zeros(shape, src.dtype(), src.device())?;
+            self.all_data = Some(ad)
+        };
+        let ad = self.all_data.as_mut().unwrap();
         if self.current_seq_len + seq_len > self.max_seq_len {
             candle::bail!(
                 "kv-cache: above max-seq-len {}+{seq_len}>{}",
@@ -60,8 +69,7 @@ impl Cache {
                 self.max_seq_len
             )
         }
-        self.all_data
-            .slice_set(src, self.dim, self.current_seq_len)?;
+        ad.slice_set(src, self.dim, self.current_seq_len)?;
         self.current_seq_len += seq_len;
         Ok(())
     }
@@ -74,17 +82,10 @@ pub struct KvCache {
 }
 
 impl KvCache {
-    pub fn new<S: Into<Shape>, D: candle::shape::Dim>(
-        dim: D,
-        shape: S,
-        dtype: DType,
-        dev: &Device,
-    ) -> Result<Self> {
-        let shape = shape.into();
-        let dim = dim.to_index(&shape, "kv-cache")?;
-        let k = Cache::new(dim, &shape, dtype, dev)?;
-        let v = Cache::new(dim, &shape, dtype, dev)?;
-        Ok(Self { k, v })
+    pub fn new(dim: usize, max_seq_len: usize) -> Self {
+        let k = Cache::new(dim, max_seq_len);
+        let v = Cache::new(dim, max_seq_len);
+        Self { k, v }
     }
 
     pub fn k_cache(&self) -> &Cache {
@@ -103,19 +104,35 @@ impl KvCache {
         &mut self.v
     }
 
-    pub fn k(&self) -> Result<Tensor> {
+    pub fn k(&self) -> Result<Option<Tensor>> {
         self.k.current_data()
     }
 
-    pub fn v(&self) -> Result<Tensor> {
+    pub fn v(&self) -> Result<Option<Tensor>> {
         self.v.current_data()
     }
 
     pub fn append(&mut self, k: &Tensor, v: &Tensor) -> Result<(Tensor, Tensor)> {
         self.k.append(k)?;
         self.v.append(v)?;
-        let k = self.k.current_data()?;
-        let v = self.v.current_data()?;
+        let out_k = self.k.current_data()?;
+        let out_v = self.v.current_data()?;
+        let k = match out_k {
+            None => {
+                let mut shape = k.dims().to_vec();
+                shape[self.k.dim] = 0;
+                Tensor::zeros(shape, k.dtype(), k.device())?
+            }
+            Some(k) => k,
+        };
+        let v = match out_v {
+            None => {
+                let mut shape = v.dims().to_vec();
+                shape[self.k.dim] = 0;
+                Tensor::zeros(shape, v.dtype(), v.device())?
+            }
+            Some(v) => v,
+        };
         Ok((k, v))
     }
 

candle-transformers/src/models/quantized_phi3.rs

@@ -203,7 +203,6 @@ fn precomput_freqs_cis(
 
 impl ModelWeights {
     pub fn from_gguf<R: std::io::Seek + std::io::Read>(
-        batch_size: usize,
         use_flash_attn: bool,
         ct: gguf_file::Content,
         reader: &mut R,
@@ -252,12 +251,7 @@ impl ModelWeights {
             )?;
             let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
             let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
-            let kv_cache = KvCache::new(
-                2,
-                (batch_size, head_count_kv, max_seq_len, head_dim),
-                DType::F32,
-                device,
-            )?;
+            let kv_cache = KvCache::new(2, max_seq_len);
             layers.push(LayerWeights {
                 attn_qkv: QLinear::new(&ct, reader, &format!("{prefix}.attn_qkv"), device)?,
                 attn_output: QLinear::new(&ct, reader, &format!("{prefix}.attn_output"), device)?,