Cuda quantization padding fix.

* Add a RotatingKVCache.

* Add some KvCache tests.

* Test the reset too.

* More kv-cache testing.

* More tests for the rotating kv-cache.

* Improve the api for the rotating cache so that the whole src tensor gets returned when it's overlarge.

* Handle contiguity + bugfix + use in mimi.

* Add a way to test the mimi streaming mode.

* Mimi streaming fixes.

* More rotating kv-cache.

* Fix the attn mask generation.

* Handle the abs case.

* Add some tests for the generated mask.

Cargo.toml

@@ -20,7 +20,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.7.0"
+version = "0.7.1"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -33,17 +33,17 @@ ab_glyph = "0.2.23"
 accelerate-src = { version = "0.3.2" }
 anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
-candle = { path = "./candle-core", package = "candle-core", version = "0.7.0" }
-candle-datasets = { path = "./candle-datasets", version = "0.7.0" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.7.0" }
-candle-kernels = { path = "./candle-kernels", version = "0.7.0" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.7.0" }
-candle-nn = { path = "./candle-nn", version = "0.7.0" }
-candle-onnx = { path = "./candle-onnx", version = "0.7.0" }
-candle-transformers = { path = "./candle-transformers", version = "0.7.0" }
+candle = { path = "./candle-core", package = "candle-core", version = "0.7.1" }
+candle-datasets = { path = "./candle-datasets", version = "0.7.1" }
+candle-flash-attn = { path = "./candle-flash-attn", version = "0.7.1" }
+candle-kernels = { path = "./candle-kernels", version = "0.7.1" }
+candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.7.1" }
+candle-nn = { path = "./candle-nn", version = "0.7.1" }
+candle-onnx = { path = "./candle-onnx", version = "0.7.1" }
+candle-transformers = { path = "./candle-transformers", version = "0.7.1" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
-cudarc = { version = "0.12.0", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false }
+cudarc = { version = "0.12.1", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false }
 fancy-regex = "0.13.0"
 gemm = { version = "0.17.0", features = ["wasm-simd128-enable"] }
 hf-hub = "0.3.0"

candle-core/src/quantized/cuda.rs

@@ -34,7 +34,10 @@ fn ceil_div(p: usize, q: usize) -> usize {
 }
 
 fn pad(p: usize, q: usize) -> usize {
-    ceil_div(p, q) * q
+    // Overallocate by q rather than just padding by q as this should pad the last row
+    // and we don't have enough information here to know how many elements to add :(
+    // ceil_div(p, q) * q
+    p + q
 }
 
 fn quantize_q8_1(
@@ -439,7 +442,7 @@ impl QCudaStorage {
             }
             _ => crate::bail!("only f32 can be quantized"),
         };
-        let src_len = src.len();
+        let src_len = pad(src.len(), MATRIX_ROW_PADDING);
         let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
         let mut qcpu_storage = crate::Device::Cpu.qzeros(src_len, self.dtype)?;
         qcpu_storage.quantize(&src)?;

candle-core/src/quantized/utils.rs

@@ -18,7 +18,7 @@ pub(super) fn group_for_quantization<'a, 'b, T: super::k_quants::GgmlType>(
     let actual_blocks = ys.len();
 
     // Validate that the input is the right size
-    if expected_blocks != actual_blocks {
+    if actual_blocks < expected_blocks {
         crate::bail!("quantize {dtype:?}: expected {expected_blocks} blocks but only {actual_blocks} were provided!")
     }
 

candle-examples/examples/flux/README.md

@@ -13,7 +13,7 @@ descriptions,
 
 ```bash
 cargo run --features cuda --example flux -r -- \
-    --height 1024 --width 1024
+    --height 1024 --width 1024 \
     --prompt "a rusty robot walking on a beach holding a small torch, the robot has the word "rust" written on it, high quality, 4k"
 ```
 

candle-examples/examples/flux/main.rs

@@ -23,6 +23,10 @@ struct Args {
     #[arg(long)]
     cpu: bool,
 
+    /// Use the quantized model.
+    #[arg(long)]
+    quantized: bool,
+
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
@@ -60,6 +64,7 @@ fn run(args: Args) -> Result<()> {
         tracing,
         decode_only,
         model,
+        quantized,
     } = args;
     let width = width.unwrap_or(1360);
     let height = height.unwrap_or(768);
@@ -146,38 +151,71 @@ fn run(args: Args) -> Result<()> {
             };
             println!("CLIP\n{clip_emb}");
             let img = {
-                let model_file = match model {
-                    Model::Schnell => bf_repo.get("flux1-schnell.safetensors")?,
-                    Model::Dev => bf_repo.get("flux1-dev.safetensors")?,
-                };
-                let vb =
-                    unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
                 let cfg = match model {
                     Model::Dev => flux::model::Config::dev(),
                     Model::Schnell => flux::model::Config::schnell(),
                 };
                 let img = flux::sampling::get_noise(1, height, width, &device)?.to_dtype(dtype)?;
-                let state = flux::sampling::State::new(&t5_emb, &clip_emb, &img)?;
+                let state = if quantized {
+                    flux::sampling::State::new(
+                        &t5_emb.to_dtype(candle::DType::F32)?,
+                        &clip_emb.to_dtype(candle::DType::F32)?,
+                        &img.to_dtype(candle::DType::F32)?,
+                    )?
+                } else {
+                    flux::sampling::State::new(&t5_emb, &clip_emb, &img)?
+                };
                 let timesteps = match model {
                     Model::Dev => {
                         flux::sampling::get_schedule(50, Some((state.img.dim(1)?, 0.5, 1.15)))
                     }
                     Model::Schnell => flux::sampling::get_schedule(4, None),
                 };
-                let model = flux::model::Flux::new(&cfg, vb)?;
-
                 println!("{state:?}");
                 println!("{timesteps:?}");
-                flux::sampling::denoise(
-                    &model,
-                    &state.img,
-                    &state.img_ids,
-                    &state.txt,
-                    &state.txt_ids,
-                    &state.vec,
-                    &timesteps,
-                    4.,
-                )?
+                if quantized {
+                    let model_file = match model {
+                        Model::Schnell => api
+                            .repo(hf_hub::Repo::model("lmz/candle-flux".to_string()))
+                            .get("flux1-schnell.gguf")?,
+                        Model::Dev => todo!(),
+                    };
+                    let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
+                        model_file, &device,
+                    )?;
+
+                    let model = flux::quantized_model::Flux::new(&cfg, vb)?;
+                    flux::sampling::denoise(
+                        &model,
+                        &state.img,
+                        &state.img_ids,
+                        &state.txt,
+                        &state.txt_ids,
+                        &state.vec,
+                        &timesteps,
+                        4.,
+                    )?
+                    .to_dtype(dtype)?
+                } else {
+                    let model_file = match model {
+                        Model::Schnell => bf_repo.get("flux1-schnell.safetensors")?,
+                        Model::Dev => bf_repo.get("flux1-dev.safetensors")?,
+                    };
+                    let vb = unsafe {
+                        VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)?
+                    };
+                    let model = flux::model::Flux::new(&cfg, vb)?;
+                    flux::sampling::denoise(
+                        &model,
+                        &state.img,
+                        &state.img_ids,
+                        &state.txt,
+                        &state.txt_ids,
+                        &state.vec,
+                        &timesteps,
+                        4.,
+                    )?
+                }
             };
             flux::sampling::unpack(&img, height, width)?
         }

candle-examples/examples/mimi/main.rs

@@ -39,6 +39,11 @@ struct Args {
     /// The model weight file, in safetensor format.
     #[arg(long)]
     model: Option<String>,
+
+    /// Whether to use streaming or not, when streaming slices of data of the given size are passed
+    /// to the encoder/decoder one at a time.
+    #[arg(long)]
+    streaming: Option<usize>,
 }
 
 fn main() -> Result<()> {
@@ -87,20 +92,49 @@ fn main() -> Result<()> {
                     pcm
                 }
             };
-            let pcm_len = pcm.len();
-            let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
-            println!("input pcm shape: {:?}", pcm.shape());
-            model.encode(&pcm)?
+            match args.streaming {
+                Some(chunk_size) => {
+                    let mut code_chunks = vec![];
+                    for pcm in pcm.chunks(chunk_size) {
+                        let pcm = Tensor::new(pcm, &device)?.reshape((1, 1, ()))?;
+                        let code_chunk = model.encode(&pcm)?;
+                        code_chunks.push(code_chunk)
+                    }
+                    Tensor::cat(&code_chunks, candle::D::Minus1)?
+                }
+                None => {
+                    let pcm_len = pcm.len();
+                    let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
+                    println!("input pcm shape: {:?}", pcm.shape());
+                    model.encode(&pcm)?
+                }
+            }
         }
     };
     println!("codes shape: {:?}", codes.shape());
+    model.reset_state();
 
     match args.action {
         Action::AudioToCode => {
             codes.save_safetensors("codes", &args.out_file)?;
         }
         Action::AudioToAudio | Action::CodeToAudio => {
-            let pcm = model.decode(&codes)?;
+            let pcm = match args.streaming {
+                Some(chunk_size) => {
+                    let seq_len = codes.dim(candle::D::Minus1)?;
+                    let mut pcm_chunks = vec![];
+                    for chunk_start in (0..seq_len).step_by(chunk_size) {
+                        let chunk_len = usize::min(chunk_size, seq_len - chunk_start);
+                        let codes = codes.narrow(candle::D::Minus1, chunk_start, chunk_len)?;
+                        let pcm = model.decode_step(&codes.into())?;
+                        if let Some(pcm) = pcm.as_option() {
+                            pcm_chunks.push(pcm.clone())
+                        }
+                    }
+                    Tensor::cat(&pcm_chunks, candle::D::Minus1)?
+                }
+                None => model.decode(&codes)?,
+            };
             println!("output pcm shape: {:?}", pcm.shape());
             let pcm = pcm.i(0)?.i(0)?;
             let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.7.0"
+version = "0.7.1"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.7.0" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.7.1" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.7.0"
+version = "0.7.1"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-metal-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.7.0"
+version = "0.7.1"
 edition = "2021"
 
 description = "Metal kernels for Candle"

candle-nn/src/kv_cache.rs

@@ -1,4 +1,4 @@
-use candle::{Result, Tensor};
+use candle::{Device, Result, Tensor};
 
 #[derive(Debug, Clone)]
 pub struct Cache {
@@ -145,3 +145,225 @@ impl KvCache {
         self.v.reset();
     }
 }
+
+#[derive(Debug, Clone)]
+pub struct RotatingCache {
+    all_data: Option<Tensor>,
+    dim: usize,
+    // `offset` is the current write index in the buffer
+    offset: usize,
+    // The total size of the sequence seen so far.
+    current_seq_len: usize,
+    // max_seq_len is the size of the rotating buffer, it is actually allowed for the full
+    // sequence to grow past this limit.
+    max_seq_len: usize,
+}
+
+impl RotatingCache {
+    pub fn new(dim: usize, max_seq_len: usize) -> Self {
+        Self {
+            all_data: None,
+            dim,
+            offset: 0,
+            current_seq_len: 0,
+            max_seq_len,
+        }
+    }
+
+    pub fn offset(&self) -> usize {
+        self.offset
+    }
+
+    pub fn dim(&self) -> usize {
+        self.dim
+    }
+
+    pub fn current_seq_len(&self) -> usize {
+        self.current_seq_len
+    }
+
+    pub fn max_seq_len(&self) -> usize {
+        self.max_seq_len
+    }
+
+    pub fn all_data(&self) -> &Option<Tensor> {
+        &self.all_data
+    }
+
+    pub fn current_data(&self) -> Result<Option<Tensor>> {
+        let data = match self.all_data.as_ref() {
+            None => None,
+            Some(d) => {
+                if self.current_seq_len >= self.max_seq_len {
+                    Some(d.clone())
+                } else {
+                    Some(d.narrow(self.dim, 0, self.current_seq_len)?)
+                }
+            }
+        };
+        Ok(data)
+    }
+
+    pub fn reset(&mut self) {
+        self.offset = 0;
+        self.current_seq_len = 0;
+        self.all_data = None;
+    }
+
+    pub fn append(&mut self, src: &Tensor) -> Result<Tensor> {
+        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();
+
+        self.current_seq_len += seq_len;
+        if seq_len >= self.max_seq_len {
+            let to_copy = src
+                .narrow(self.dim, seq_len - self.max_seq_len, self.max_seq_len)?
+                .contiguous()?;
+            ad.slice_set(&to_copy, self.dim, 0)?;
+            self.offset = 0;
+            // Here we return `src` rather than `ad` so that all the past can be used.
+            Ok(src.clone())
+        } else {
+            let rem_len = self.max_seq_len - self.offset;
+            if seq_len <= rem_len {
+                ad.slice_set(&src.contiguous()?, self.dim, self.offset)?;
+                self.offset = (self.offset + seq_len) % self.max_seq_len;
+            } else {
+                // We have to make two copies here as we go over the boundary of the cache.
+                if rem_len > 0 {
+                    let src1 = src.narrow(self.dim, 0, rem_len)?.contiguous()?;
+                    ad.slice_set(&src1, self.dim, self.offset)?;
+                }
+                let src2 = src
+                    .narrow(self.dim, rem_len, seq_len - rem_len)?
+                    .contiguous()?;
+                ad.slice_set(&src2, self.dim, 0)?;
+                self.offset = seq_len - rem_len;
+            }
+            if self.current_seq_len >= self.max_seq_len {
+                Ok(ad.clone())
+            } else {
+                Ok(ad.narrow(self.dim, 0, self.current_seq_len)?)
+            }
+        }
+    }
+
+    fn get_mask_abs(&self, size1: usize, size2: usize, device: &Device) -> Result<Tensor> {
+        let context = self.max_seq_len;
+        let mask: Vec<_> = (0..size1)
+            .flat_map(|i| {
+                (0..size2).map(move |j| {
+                    u8::from(size1 + j > size2 + i || size1 + j + context < size2 + i)
+                })
+            })
+            .collect();
+        Tensor::from_slice(&mask, (size1, size2), device)
+    }
+
+    fn get_mask_rel(&self, size1: usize, size2: usize, device: &Device) -> Result<Tensor> {
+        let context = self.max_seq_len;
+        let upd_offset = (self.offset + size1) % self.max_seq_len;
+        let mask: Vec<_> = (0..size1)
+            .flat_map(|pos_src| {
+                // The absolute position of the elements that will get added to the cache.
+                let pos_src = self.current_seq_len + pos_src;
+                (0..size2).map(move |pos_cache_rel| {
+                    // The absolute position of the cache elements after the addition.
+                    let pos_cache = self.current_seq_len + size1 + pos_cache_rel - upd_offset;
+                    let pos_cache = if pos_cache_rel < upd_offset {
+                        pos_cache
+                    } else {
+                        pos_cache - self.max_seq_len
+                    };
+                    u8::from(pos_cache > pos_src || pos_cache + context < pos_src)
+                })
+            })
+            .collect();
+        Tensor::from_slice(&mask, (size1, size2), device)
+    }
+
+    /// Returns the attn_mask to be applied *after* adding `seq_len` to the cache.
+    pub fn attn_mask(&self, seq_len: usize, device: &Device) -> Result<Option<Tensor>> {
+        let mask = if seq_len == 1 {
+            None
+        } else {
+            let mask = if seq_len < self.max_seq_len {
+                let cache_out_len = (self.current_seq_len + seq_len).min(self.max_seq_len);
+                self.get_mask_rel(seq_len, cache_out_len, device)?
+            } else {
+                self.get_mask_abs(seq_len, seq_len, device)?
+            };
+            Some(mask)
+        };
+        Ok(mask)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct RotatingKvCache {
+    k: RotatingCache,
+    v: RotatingCache,
+}
+
+impl RotatingKvCache {
+    pub fn new(dim: usize, max_seq_len: usize) -> Self {
+        let k = RotatingCache::new(dim, max_seq_len);
+        let v = RotatingCache::new(dim, max_seq_len);
+        Self { k, v }
+    }
+
+    pub fn k_cache(&self) -> &RotatingCache {
+        &self.k
+    }
+
+    pub fn v_cache(&self) -> &RotatingCache {
+        &self.v
+    }
+
+    pub fn k_cache_mut(&mut self) -> &mut RotatingCache {
+        &mut self.k
+    }
+
+    pub fn v_cache_mut(&mut self) -> &mut RotatingCache {
+        &mut self.v
+    }
+
+    pub fn k(&self) -> Result<Option<Tensor>> {
+        self.k.current_data()
+    }
+
+    pub fn v(&self) -> Result<Option<Tensor>> {
+        self.v.current_data()
+    }
+
+    pub fn append(&mut self, k: &Tensor, v: &Tensor) -> Result<(Tensor, Tensor)> {
+        let out_k = self.k.append(k)?;
+        let out_v = self.v.append(v)?;
+        Ok((out_k, out_v))
+    }
+
+    pub fn offset(&self) -> usize {
+        self.k.offset()
+    }
+
+    pub fn current_seq_len(&self) -> usize {
+        self.k.current_seq_len()
+    }
+
+    pub fn attn_mask(&self, seq_len: usize, device: &Device) -> Result<Option<Tensor>> {
+        self.k.attn_mask(seq_len, device)
+    }
+
+    pub fn reset(&mut self) {
+        self.k.reset();
+        self.v.reset();
+    }
+}

candle-nn/tests/kv_cache.rs

@@ -0,0 +1,110 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{Device, Result, Tensor};
+
+#[test]
+fn kv_cache() -> Result<()> {
+    let mut cache = candle_nn::kv_cache::Cache::new(0, 16);
+    for _ in [0, 1] {
+        assert_eq!(cache.current_seq_len(), 0);
+        let data = cache.current_data()?;
+        assert!(data.is_none());
+        let t = Tensor::new(&[1f32, 2., 3.], &Device::Cpu)?;
+        cache.append(&t)?;
+        let data = cache.current_data()?.unwrap();
+        assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3.]);
+        let t = Tensor::new(&[4f32], &Device::Cpu)?;
+        cache.append(&t)?;
+        let data = cache.current_data()?.unwrap();
+        assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3., 4.]);
+        let t = Tensor::new(&[0f32, 5., 6., 7.], &Device::Cpu)?;
+        cache.append(&t)?;
+        let data = cache.current_data()?.unwrap();
+        assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3., 4., 0., 5., 6., 7.]);
+        assert_eq!(cache.current_seq_len(), 8);
+        cache.reset();
+    }
+    Ok(())
+}
+
+#[test]
+fn rotating_kv_cache() -> Result<()> {
+    let mut cache = candle_nn::kv_cache::RotatingCache::new(0, 6);
+    for _ in [0, 1] {
+        assert_eq!(cache.offset(), 0);
+        assert_eq!(cache.current_seq_len(), 0);
+        let data = cache.current_data()?;
+        assert!(data.is_none());
+        let t = Tensor::new(&[1., 2., 3.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [1., 2., 3.]);
+        let t = Tensor::new(&[4.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [1., 2., 3., 4.]);
+        let t = Tensor::new(&[0., 5., 6., 7.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [6., 7., 3., 4., 0., 5.]);
+        assert_eq!(cache.current_seq_len(), 8);
+        assert_eq!(cache.offset(), 2);
+
+        let t = Tensor::new(&[8.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [6., 7., 8., 4., 0., 5.]);
+        assert_eq!(cache.current_seq_len(), 9);
+        assert_eq!(cache.offset(), 3);
+
+        let t = Tensor::new(&[9., 10., 11.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [6., 7., 8., 9., 10., 11.]);
+        assert_eq!(cache.current_seq_len(), 12);
+        assert_eq!(cache.offset(), 0);
+
+        let t = Tensor::new(&[12.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [12., 7., 8., 9., 10., 11.]);
+        assert_eq!(cache.current_seq_len(), 13);
+        assert_eq!(cache.offset(), 1);
+
+        let mask = cache.attn_mask(2, &Device::Cpu)?.unwrap();
+        assert_eq!(
+            mask.to_vec2::<u8>()?,
+            &[[0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+        );
+        let mask = cache.attn_mask(3, &Device::Cpu)?.unwrap();
+        assert_eq!(
+            mask.to_vec2::<u8>()?,
+            &[[0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0]],
+        );
+        let t = Tensor::new(&[0., 1., 2., 3., 4., 5., 6., 7., 8.], &Device::Cpu)?;
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [0., 1., 2., 3., 4., 5., 6., 7., 8.]);
+        assert_eq!(cache.current_seq_len(), 22);
+        assert_eq!(cache.offset(), 0);
+
+        let mask = cache.attn_mask(1, &Device::Cpu)?;
+        assert!(mask.is_none());
+        let mask = cache.attn_mask(2, &Device::Cpu)?.unwrap();
+        assert_eq!(
+            mask.to_vec2::<u8>()?,
+            &[[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+        );
+        let mask = cache.attn_mask(3, &Device::Cpu)?.unwrap();
+        assert_eq!(
+            mask.to_vec2::<u8>()?,
+            &[[0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+        );
+        let t = Tensor::new(&[42.], &Device::Cpu)?;
+
+        let data = cache.append(&t)?;
+        assert_eq!(data.to_vec1::<f64>()?, [42., 4., 5., 6., 7., 8.]);
+        assert_eq!(cache.current_seq_len(), 23);
+        assert_eq!(cache.offset(), 1);
+
+        cache.reset();
+    }
+    Ok(())
+}

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.7.0"
+version = "0.7.1"
 edition = "2021"
 
 description = "ONNX support for Candle"
@@ -10,8 +10,8 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-candle = { path = "../candle-core", package = "candle-core", version = "0.7.0" }
-candle-nn = { path = "../candle-nn", version = "0.7.0" }
+candle = { path = "../candle-core", package = "candle-core", version = "0.7.1" }
+candle-nn = { path = "../candle-nn", version = "0.7.1" }
 prost = "0.12.1"
 
 [build-dependencies]

candle-transformers/src/models/flux/mod.rs

@@ -1,3 +1,20 @@
+use candle::{Result, Tensor};
+
+pub trait WithForward {
+    #[allow(clippy::too_many_arguments)]
+    fn forward(
+        &self,
+        img: &Tensor,
+        img_ids: &Tensor,
+        txt: &Tensor,
+        txt_ids: &Tensor,
+        timesteps: &Tensor,
+        y: &Tensor,
+        guidance: Option<&Tensor>,
+    ) -> Result<Tensor>;
+}
+
 pub mod autoencoder;
 pub mod model;
+pub mod quantized_model;
 pub mod sampling;

candle-transformers/src/models/flux/model.rs

@@ -109,14 +109,14 @@ fn apply_rope(x: &Tensor, freq_cis: &Tensor) -> Result<Tensor> {
     (fr0.broadcast_mul(&x0)? + fr1.broadcast_mul(&x1)?)?.reshape(dims.to_vec())
 }
 
-fn attention(q: &Tensor, k: &Tensor, v: &Tensor, pe: &Tensor) -> Result<Tensor> {
+pub(crate) fn attention(q: &Tensor, k: &Tensor, v: &Tensor, pe: &Tensor) -> Result<Tensor> {
     let q = apply_rope(q, pe)?.contiguous()?;
     let k = apply_rope(k, pe)?.contiguous()?;
     let x = scaled_dot_product_attention(&q, &k, v)?;
     x.transpose(1, 2)?.flatten_from(2)
 }
 
-fn timestep_embedding(t: &Tensor, dim: usize, dtype: DType) -> Result<Tensor> {
+pub(crate) fn timestep_embedding(t: &Tensor, dim: usize, dtype: DType) -> Result<Tensor> {
     const TIME_FACTOR: f64 = 1000.;
     const MAX_PERIOD: f64 = 10000.;
     if dim % 2 == 1 {
@@ -144,7 +144,7 @@ pub struct EmbedNd {
 }
 
 impl EmbedNd {
-    fn new(dim: usize, theta: usize, axes_dim: Vec<usize>) -> Self {
+    pub fn new(dim: usize, theta: usize, axes_dim: Vec<usize>) -> Self {
         Self {
             dim,
             theta,
@@ -575,9 +575,11 @@ impl Flux {
             final_layer,
         })
     }
+}
 
+impl super::WithForward for Flux {
     #[allow(clippy::too_many_arguments)]
-    pub fn forward(
+    fn forward(
         &self,
         img: &Tensor,
         img_ids: &Tensor,

candle-transformers/src/models/flux/quantized_model.rs

@@ -0,0 +1,465 @@
+use super::model::{attention, timestep_embedding, Config, EmbedNd};
+use crate::quantized_nn::{linear, linear_b, Linear};
+use crate::quantized_var_builder::VarBuilder;
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{LayerNorm, RmsNorm};
+
+fn layer_norm(dim: usize, vb: VarBuilder) -> Result<LayerNorm> {
+    let ws = Tensor::ones(dim, DType::F32, vb.device())?;
+    Ok(LayerNorm::new_no_bias(ws, 1e-6))
+}
+
+#[derive(Debug, Clone)]
+pub struct MlpEmbedder {
+    in_layer: Linear,
+    out_layer: Linear,
+}
+
+impl MlpEmbedder {
+    fn new(in_sz: usize, h_sz: usize, vb: VarBuilder) -> Result<Self> {
+        let in_layer = linear(in_sz, h_sz, vb.pp("in_layer"))?;
+        let out_layer = linear(h_sz, h_sz, vb.pp("out_layer"))?;
+        Ok(Self {
+            in_layer,
+            out_layer,
+        })
+    }
+}
+
+impl candle::Module for MlpEmbedder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.in_layer)?.silu()?.apply(&self.out_layer)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct QkNorm {
+    query_norm: RmsNorm,
+    key_norm: RmsNorm,
+}
+
+impl QkNorm {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let query_norm = vb.get(dim, "query_norm.scale")?.dequantize(vb.device())?;
+        let query_norm = RmsNorm::new(query_norm, 1e-6);
+        let key_norm = vb.get(dim, "key_norm.scale")?.dequantize(vb.device())?;
+        let key_norm = RmsNorm::new(key_norm, 1e-6);
+        Ok(Self {
+            query_norm,
+            key_norm,
+        })
+    }
+}
+
+struct ModulationOut {
+    shift: Tensor,
+    scale: Tensor,
+    gate: Tensor,
+}
+
+impl ModulationOut {
+    fn scale_shift(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.broadcast_mul(&(&self.scale + 1.)?)?
+            .broadcast_add(&self.shift)
+    }
+
+    fn gate(&self, xs: &Tensor) -> Result<Tensor> {
+        self.gate.broadcast_mul(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Modulation1 {
+    lin: Linear,
+}
+
+impl Modulation1 {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let lin = linear(dim, 3 * dim, vb.pp("lin"))?;
+        Ok(Self { lin })
+    }
+
+    fn forward(&self, vec_: &Tensor) -> Result<ModulationOut> {
+        let ys = vec_
+            .silu()?
+            .apply(&self.lin)?
+            .unsqueeze(1)?
+            .chunk(3, D::Minus1)?;
+        if ys.len() != 3 {
+            candle::bail!("unexpected len from chunk {ys:?}")
+        }
+        Ok(ModulationOut {
+            shift: ys[0].clone(),
+            scale: ys[1].clone(),
+            gate: ys[2].clone(),
+        })
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Modulation2 {
+    lin: Linear,
+}
+
+impl Modulation2 {
+    fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let lin = linear(dim, 6 * dim, vb.pp("lin"))?;
+        Ok(Self { lin })
+    }
+
+    fn forward(&self, vec_: &Tensor) -> Result<(ModulationOut, ModulationOut)> {
+        let ys = vec_
+            .silu()?
+            .apply(&self.lin)?
+            .unsqueeze(1)?
+            .chunk(6, D::Minus1)?;
+        if ys.len() != 6 {
+            candle::bail!("unexpected len from chunk {ys:?}")
+        }
+        let mod1 = ModulationOut {
+            shift: ys[0].clone(),
+            scale: ys[1].clone(),
+            gate: ys[2].clone(),
+        };
+        let mod2 = ModulationOut {
+            shift: ys[3].clone(),
+            scale: ys[4].clone(),
+            gate: ys[5].clone(),
+        };
+        Ok((mod1, mod2))
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct SelfAttention {
+    qkv: Linear,
+    norm: QkNorm,
+    proj: Linear,
+    num_heads: usize,
+}
+
+impl SelfAttention {
+    fn new(dim: usize, num_heads: usize, qkv_bias: bool, vb: VarBuilder) -> Result<Self> {
+        let head_dim = dim / num_heads;
+        let qkv = linear_b(dim, dim * 3, qkv_bias, vb.pp("qkv"))?;
+        let norm = QkNorm::new(head_dim, vb.pp("norm"))?;
+        let proj = linear(dim, dim, vb.pp("proj"))?;
+        Ok(Self {
+            qkv,
+            norm,
+            proj,
+            num_heads,
+        })
+    }
+
+    fn qkv(&self, xs: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
+        let qkv = xs.apply(&self.qkv)?;
+        let (b, l, _khd) = qkv.dims3()?;
+        let qkv = qkv.reshape((b, l, 3, self.num_heads, ()))?;
+        let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
+        let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
+        let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
+        let q = q.apply(&self.norm.query_norm)?;
+        let k = k.apply(&self.norm.key_norm)?;
+        Ok((q, k, v))
+    }
+
+    #[allow(unused)]
+    fn forward(&self, xs: &Tensor, pe: &Tensor) -> Result<Tensor> {
+        let (q, k, v) = self.qkv(xs)?;
+        attention(&q, &k, &v, pe)?.apply(&self.proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    lin1: Linear,
+    lin2: Linear,
+}
+
+impl Mlp {
+    fn new(in_sz: usize, mlp_sz: usize, vb: VarBuilder) -> Result<Self> {
+        let lin1 = linear(in_sz, mlp_sz, vb.pp("0"))?;
+        let lin2 = linear(mlp_sz, in_sz, vb.pp("2"))?;
+        Ok(Self { lin1, lin2 })
+    }
+}
+
+impl candle::Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.lin1)?.gelu()?.apply(&self.lin2)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct DoubleStreamBlock {
+    img_mod: Modulation2,
+    img_norm1: LayerNorm,
+    img_attn: SelfAttention,
+    img_norm2: LayerNorm,
+    img_mlp: Mlp,
+    txt_mod: Modulation2,
+    txt_norm1: LayerNorm,
+    txt_attn: SelfAttention,
+    txt_norm2: LayerNorm,
+    txt_mlp: Mlp,
+}
+
+impl DoubleStreamBlock {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let h_sz = cfg.hidden_size;
+        let mlp_sz = (h_sz as f64 * cfg.mlp_ratio) as usize;
+        let img_mod = Modulation2::new(h_sz, vb.pp("img_mod"))?;
+        let img_norm1 = layer_norm(h_sz, vb.pp("img_norm1"))?;
+        let img_attn = SelfAttention::new(h_sz, cfg.num_heads, cfg.qkv_bias, vb.pp("img_attn"))?;
+        let img_norm2 = layer_norm(h_sz, vb.pp("img_norm2"))?;
+        let img_mlp = Mlp::new(h_sz, mlp_sz, vb.pp("img_mlp"))?;
+        let txt_mod = Modulation2::new(h_sz, vb.pp("txt_mod"))?;
+        let txt_norm1 = layer_norm(h_sz, vb.pp("txt_norm1"))?;
+        let txt_attn = SelfAttention::new(h_sz, cfg.num_heads, cfg.qkv_bias, vb.pp("txt_attn"))?;
+        let txt_norm2 = layer_norm(h_sz, vb.pp("txt_norm2"))?;
+        let txt_mlp = Mlp::new(h_sz, mlp_sz, vb.pp("txt_mlp"))?;
+        Ok(Self {
+            img_mod,
+            img_norm1,
+            img_attn,
+            img_norm2,
+            img_mlp,
+            txt_mod,
+            txt_norm1,
+            txt_attn,
+            txt_norm2,
+            txt_mlp,
+        })
+    }
+
+    fn forward(
+        &self,
+        img: &Tensor,
+        txt: &Tensor,
+        vec_: &Tensor,
+        pe: &Tensor,
+    ) -> Result<(Tensor, Tensor)> {
+        let (img_mod1, img_mod2) = self.img_mod.forward(vec_)?; // shift, scale, gate
+        let (txt_mod1, txt_mod2) = self.txt_mod.forward(vec_)?; // shift, scale, gate
+        let img_modulated = img.apply(&self.img_norm1)?;
+        let img_modulated = img_mod1.scale_shift(&img_modulated)?;
+        let (img_q, img_k, img_v) = self.img_attn.qkv(&img_modulated)?;
+
+        let txt_modulated = txt.apply(&self.txt_norm1)?;
+        let txt_modulated = txt_mod1.scale_shift(&txt_modulated)?;
+        let (txt_q, txt_k, txt_v) = self.txt_attn.qkv(&txt_modulated)?;
+
+        let q = Tensor::cat(&[txt_q, img_q], 2)?;
+        let k = Tensor::cat(&[txt_k, img_k], 2)?;
+        let v = Tensor::cat(&[txt_v, img_v], 2)?;
+
+        let attn = attention(&q, &k, &v, pe)?;
+        let txt_attn = attn.narrow(1, 0, txt.dim(1)?)?;
+        let img_attn = attn.narrow(1, txt.dim(1)?, attn.dim(1)? - txt.dim(1)?)?;
+
+        let img = (img + img_mod1.gate(&img_attn.apply(&self.img_attn.proj)?))?;
+        let img = (&img
+            + img_mod2.gate(
+                &img_mod2
+                    .scale_shift(&img.apply(&self.img_norm2)?)?
+                    .apply(&self.img_mlp)?,
+            )?)?;
+
+        let txt = (txt + txt_mod1.gate(&txt_attn.apply(&self.txt_attn.proj)?))?;
+        let txt = (&txt
+            + txt_mod2.gate(
+                &txt_mod2
+                    .scale_shift(&txt.apply(&self.txt_norm2)?)?
+                    .apply(&self.txt_mlp)?,
+            )?)?;
+
+        Ok((img, txt))
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct SingleStreamBlock {
+    linear1: Linear,
+    linear2: Linear,
+    norm: QkNorm,
+    pre_norm: LayerNorm,
+    modulation: Modulation1,
+    h_sz: usize,
+    mlp_sz: usize,
+    num_heads: usize,
+}
+
+impl SingleStreamBlock {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let h_sz = cfg.hidden_size;
+        let mlp_sz = (h_sz as f64 * cfg.mlp_ratio) as usize;
+        let head_dim = h_sz / cfg.num_heads;
+        let linear1 = linear(h_sz, h_sz * 3 + mlp_sz, vb.pp("linear1"))?;
+        let linear2 = linear(h_sz + mlp_sz, h_sz, vb.pp("linear2"))?;
+        let norm = QkNorm::new(head_dim, vb.pp("norm"))?;
+        let pre_norm = layer_norm(h_sz, vb.pp("pre_norm"))?;
+        let modulation = Modulation1::new(h_sz, vb.pp("modulation"))?;
+        Ok(Self {
+            linear1,
+            linear2,
+            norm,
+            pre_norm,
+            modulation,
+            h_sz,
+            mlp_sz,
+            num_heads: cfg.num_heads,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, vec_: &Tensor, pe: &Tensor) -> Result<Tensor> {
+        let mod_ = self.modulation.forward(vec_)?;
+        let x_mod = mod_.scale_shift(&xs.apply(&self.pre_norm)?)?;
+        let x_mod = x_mod.apply(&self.linear1)?;
+        let qkv = x_mod.narrow(D::Minus1, 0, 3 * self.h_sz)?;
+        let (b, l, _khd) = qkv.dims3()?;
+        let qkv = qkv.reshape((b, l, 3, self.num_heads, ()))?;
+        let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
+        let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
+        let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
+        let mlp = x_mod.narrow(D::Minus1, 3 * self.h_sz, self.mlp_sz)?;
+        let q = q.apply(&self.norm.query_norm)?;
+        let k = k.apply(&self.norm.key_norm)?;
+        let attn = attention(&q, &k, &v, pe)?;
+        let output = Tensor::cat(&[attn, mlp.gelu()?], 2)?.apply(&self.linear2)?;
+        xs + mod_.gate(&output)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct LastLayer {
+    norm_final: LayerNorm,
+    linear: Linear,
+    ada_ln_modulation: Linear,
+}
+
+impl LastLayer {
+    fn new(h_sz: usize, p_sz: usize, out_c: usize, vb: VarBuilder) -> Result<Self> {
+        let norm_final = layer_norm(h_sz, vb.pp("norm_final"))?;
+        let linear_ = linear(h_sz, p_sz * p_sz * out_c, vb.pp("linear"))?;
+        let ada_ln_modulation = linear(h_sz, 2 * h_sz, vb.pp("adaLN_modulation.1"))?;
+        Ok(Self {
+            norm_final,
+            linear: linear_,
+            ada_ln_modulation,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor, vec: &Tensor) -> Result<Tensor> {
+        let chunks = vec.silu()?.apply(&self.ada_ln_modulation)?.chunk(2, 1)?;
+        let (shift, scale) = (&chunks[0], &chunks[1]);
+        let xs = xs
+            .apply(&self.norm_final)?
+            .broadcast_mul(&(scale.unsqueeze(1)? + 1.0)?)?
+            .broadcast_add(&shift.unsqueeze(1)?)?;
+        xs.apply(&self.linear)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Flux {
+    img_in: Linear,
+    txt_in: Linear,
+    time_in: MlpEmbedder,
+    vector_in: MlpEmbedder,
+    guidance_in: Option<MlpEmbedder>,
+    pe_embedder: EmbedNd,
+    double_blocks: Vec<DoubleStreamBlock>,
+    single_blocks: Vec<SingleStreamBlock>,
+    final_layer: LastLayer,
+}
+
+impl Flux {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let img_in = linear(cfg.in_channels, cfg.hidden_size, vb.pp("img_in"))?;
+        let txt_in = linear(cfg.context_in_dim, cfg.hidden_size, vb.pp("txt_in"))?;
+        let mut double_blocks = Vec::with_capacity(cfg.depth);
+        let vb_d = vb.pp("double_blocks");
+        for idx in 0..cfg.depth {
+            let db = DoubleStreamBlock::new(cfg, vb_d.pp(idx))?;
+            double_blocks.push(db)
+        }
+        let mut single_blocks = Vec::with_capacity(cfg.depth_single_blocks);
+        let vb_s = vb.pp("single_blocks");
+        for idx in 0..cfg.depth_single_blocks {
+            let sb = SingleStreamBlock::new(cfg, vb_s.pp(idx))?;
+            single_blocks.push(sb)
+        }
+        let time_in = MlpEmbedder::new(256, cfg.hidden_size, vb.pp("time_in"))?;
+        let vector_in = MlpEmbedder::new(cfg.vec_in_dim, cfg.hidden_size, vb.pp("vector_in"))?;
+        let guidance_in = if cfg.guidance_embed {
+            let mlp = MlpEmbedder::new(256, cfg.hidden_size, vb.pp("guidance_in"))?;
+            Some(mlp)
+        } else {
+            None
+        };
+        let final_layer =
+            LastLayer::new(cfg.hidden_size, 1, cfg.in_channels, vb.pp("final_layer"))?;
+        let pe_dim = cfg.hidden_size / cfg.num_heads;
+        let pe_embedder = EmbedNd::new(pe_dim, cfg.theta, cfg.axes_dim.to_vec());
+        Ok(Self {
+            img_in,
+            txt_in,
+            time_in,
+            vector_in,
+            guidance_in,
+            pe_embedder,
+            double_blocks,
+            single_blocks,
+            final_layer,
+        })
+    }
+}
+
+impl super::WithForward for Flux {
+    #[allow(clippy::too_many_arguments)]
+    fn forward(
+        &self,
+        img: &Tensor,
+        img_ids: &Tensor,
+        txt: &Tensor,
+        txt_ids: &Tensor,
+        timesteps: &Tensor,
+        y: &Tensor,
+        guidance: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        if txt.rank() != 3 {
+            candle::bail!("unexpected shape for txt {:?}", txt.shape())
+        }
+        if img.rank() != 3 {
+            candle::bail!("unexpected shape for img {:?}", img.shape())
+        }
+        let dtype = img.dtype();
+        let pe = {
+            let ids = Tensor::cat(&[txt_ids, img_ids], 1)?;
+            ids.apply(&self.pe_embedder)?
+        };
+        let mut txt = txt.apply(&self.txt_in)?;
+        let mut img = img.apply(&self.img_in)?;
+        let vec_ = timestep_embedding(timesteps, 256, dtype)?.apply(&self.time_in)?;
+        let vec_ = match (self.guidance_in.as_ref(), guidance) {
+            (Some(g_in), Some(guidance)) => {
+                (vec_ + timestep_embedding(guidance, 256, dtype)?.apply(g_in))?
+            }
+            _ => vec_,
+        };
+        let vec_ = (vec_ + y.apply(&self.vector_in))?;
+
+        // Double blocks
+        for block in self.double_blocks.iter() {
+            (img, txt) = block.forward(&img, &txt, &vec_, &pe)?
+        }
+        // Single blocks
+        let mut img = Tensor::cat(&[&txt, &img], 1)?;
+        for block in self.single_blocks.iter() {
+            img = block.forward(&img, &vec_, &pe)?;
+        }
+        let img = img.i((.., txt.dim(1)?..))?;
+        self.final_layer.forward(&img, &vec_)
+    }
+}

candle-transformers/src/models/flux/sampling.rs

@@ -92,8 +92,8 @@ pub fn unpack(xs: &Tensor, height: usize, width: usize) -> Result<Tensor> {
 }
 
 #[allow(clippy::too_many_arguments)]
-pub fn denoise(
-    model: &super::model::Flux,
+pub fn denoise<M: super::WithForward>(
+    model: &M,
     img: &Tensor,
     img_ids: &Tensor,
     txt: &Tensor,

candle-transformers/src/models/mimi/transformer.rs

@@ -101,21 +101,6 @@ impl Module for LayerScale {
     }
 }
 
-pub(crate) fn get_mask(
-    size1: usize,
-    size2: usize,
-    context: usize,
-    device: &Device,
-) -> Result<Tensor> {
-    let mask: Vec<_> = (0..size1)
-        .flat_map(|i| {
-            (0..size2)
-                .map(move |j| u8::from(size1 + j > size2 + i || size1 + j + context < size2 + i))
-        })
-        .collect();
-    Tensor::from_slice(&mask, (size1, size2), device)
-}
-
 #[derive(Debug, Clone)]
 pub struct StreamingMultiheadAttention {
     q_proj: Linear,
@@ -127,7 +112,7 @@ pub struct StreamingMultiheadAttention {
     context: usize,
     neg_inf: Tensor,
     rope: Option<Arc<RotaryEmbedding>>,
-    kv_cache: candle_nn::kv_cache::KvCache,
+    kv_cache: candle_nn::kv_cache::RotatingKvCache,
     pos: usize,
     use_flash_attn: bool,
     span: tracing::Span,
@@ -153,7 +138,7 @@ impl StreamingMultiheadAttention {
             num_heads: cfg.num_heads,
             context: cfg.context,
             neg_inf,
-            kv_cache: candle_nn::kv_cache::KvCache::new(2, cfg.max_seq_len),
+            kv_cache: candle_nn::kv_cache::RotatingKvCache::new(2, cfg.context),
             pos: 0,
             use_flash_attn: false,
             span: tracing::span!(tracing::Level::TRACE, "mha"),
@@ -236,7 +221,7 @@ impl StreamingMultiheadAttention {
         self.kv_cache.reset()
     }
 
-    pub fn set_kv_cache(&mut self, kv_cache: candle_nn::kv_cache::KvCache) {
+    pub fn set_kv_cache(&mut self, kv_cache: candle_nn::kv_cache::RotatingKvCache) {
         self.kv_cache = kv_cache
     }
 }
@@ -582,7 +567,7 @@ impl StreamingTransformerLayer {
         self.self_attn.reset_kv_cache()
     }
 
-    pub fn set_kv_cache(&mut self, kv_cache: candle_nn::kv_cache::KvCache) {
+    pub fn set_kv_cache(&mut self, kv_cache: candle_nn::kv_cache::RotatingKvCache) {
         self.self_attn.set_kv_cache(kv_cache)
     }
 }
@@ -590,7 +575,6 @@ impl StreamingTransformerLayer {
 #[derive(Debug, Clone)]
 pub struct StreamingTransformer {
     layers: Vec<StreamingTransformerLayer>,
-    context: usize,
     positional_embedding: PositionalEmbedding,
     max_period: usize,
 }
@@ -617,7 +601,6 @@ impl StreamingTransformer {
         }
         Ok(Self {
             layers,
-            context: cfg.context,
             positional_embedding: cfg.positional_embedding,
             max_period: cfg.max_period,
         })
@@ -629,19 +612,11 @@ impl StreamingTransformer {
 
     pub fn forward_ca(&mut self, xs: &Tensor, ca_src: Option<&Tensor>) -> Result<Tensor> {
         let (_b, t, c) = xs.dims3()?;
-        // We will extract at most "context" from the kv_cache.
-        // Note that the mask will discard the values that are before context.
-        let pos = self.layers[0]
+        let pos = self.layers[0].self_attn.kv_cache.current_seq_len();
+        let mask = self.layers[0]
             .self_attn
             .kv_cache
-            .k_cache()
-            .current_seq_len()
-            .min(self.context);
-        let mask = if t == 1 {
-            None
-        } else {
-            Some(get_mask(t, pos + t, self.context, xs.device())?)
-        };
+            .attn_mask(t, xs.device())?;
         let mut xs = match self.positional_embedding {
             PositionalEmbedding::Rope | PositionalEmbedding::None => xs.clone(),
             PositionalEmbedding::Sin => {