Bump the crate version to 0.9.0. (#2924)

* Add the scatter_set op.

* Metal op.

* Cuda version.

* Merge the checks.

* Add the actual ops.

Cargo.toml

@@ -20,7 +20,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.9.0-alpha.4"
+version = "0.9.0"
 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 = "0.9.0-alpha.4" }
-candle-datasets = { path = "./candle-datasets", version = "0.9.0-alpha.4" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.9.0-alpha.4" }
-candle-kernels = { path = "./candle-kernels", version = "0.9.0-alpha.4" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.9.0-alpha.4" }
-candle-nn = { path = "./candle-nn", version = "0.9.0-alpha.4" }
-candle-onnx = { path = "./candle-onnx", version = "0.9.0-alpha.4" }
-candle-transformers = { path = "./candle-transformers", version = "0.9.0-alpha.4" }
+candle = { path = "./candle-core", package = "candle-core", version = "0.9.0" }
+candle-datasets = { path = "./candle-datasets", version = "0.9.0" }
+candle-flash-attn = { path = "./candle-flash-attn", version = "0.9.0" }
+candle-kernels = { path = "./candle-kernels", version = "0.9.0" }
+candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.9.0" }
+candle-nn = { path = "./candle-nn", version = "0.9.0" }
+candle-onnx = { path = "./candle-onnx", version = "0.9.0" }
+candle-transformers = { path = "./candle-transformers", version = "0.9.0" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
-cudarc = { version = "0.16.0", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false }
+cudarc = { version = "0.16.1", 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 = "0.4.1"

candle-core/src/backend.rs

@@ -71,15 +71,27 @@ pub trait BackendStorage: Sized {
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self>;
 
     fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self>;
-    fn scatter_add(
-        &self,
+
+    fn scatter_set(
+        &mut self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: usize,
-    ) -> Result<Self>;
+    ) -> Result<()>;
+
+    fn scatter_add_set(
+        &mut self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: usize,
+    ) -> Result<()>;
+
     fn index_select(&self, _: &Self, _: &Layout, _: &Layout, _: usize) -> Result<Self>;
     fn index_add(
         &self,
@@ -113,6 +125,8 @@ pub trait BackendStorage: Sized {
         _src_offset: usize,
         _dst_offset: usize,
     ) -> Result<()>;
+
+    fn const_set(&mut self, _: crate::scalar::Scalar, _: &Layout) -> Result<()>;
 }
 
 pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
@@ -127,8 +141,6 @@ pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
 
     fn zeros_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
 
-    fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
-
     /// # Safety
     /// This function is unsafe as it doesn't initialize the underlying data store.
     /// The caller should ensure that the data is properly initialized as early as possible

candle-core/src/backprop.rs

@@ -53,6 +53,7 @@ impl Tensor {
             } else if let Some(op) = node.op() {
                 match op {
                     Op::IndexAdd(t1, t2, t3, _)
+                    | Op::Scatter(t1, t2, t3, _)
                     | Op::ScatterAdd(t1, t2, t3, _)
                     | Op::CustomOp3(t1, t2, t3, _)
                     | Op::WhereCond(t1, t2, t3) => {
@@ -419,7 +420,7 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.scatter_add(indexes, &grad, *dim)?;
                     }
-                    Op::ScatterAdd(init, indexes, src, dim) => {
+                    Op::Scatter(init, indexes, src, dim) => {
                         let init_sum_grad = grads.or_insert(init)?;
                         *init_sum_grad = init_sum_grad.add(&grad)?;
 
@@ -427,6 +428,16 @@ impl Tensor {
                         let src_sum_grad = grads.or_insert(src)?;
                         *src_sum_grad = src_sum_grad.add(&src_grad)?;
                     }
+                    Op::ScatterAdd(init, indexes, src, dim) => {
+                        let init_sum_grad = grads.or_insert(init)?;
+                        let mask = init.ones_like()?;
+                        let mask = mask.scatter(indexes, &mask.zeros_like()?, *dim)?;
+                        *init_sum_grad = init_sum_grad.add(&grad.mul(&mask)?)?;
+
+                        let src_grad = grad.gather(indexes, *dim)?;
+                        let src_sum_grad = grads.or_insert(src)?;
+                        *src_sum_grad = src_sum_grad.add(&src_grad)?;
+                    }
                     Op::IndexAdd(init, indexes, src, dim) => {
                         let init_sum_grad = grads.or_insert(init)?;
                         *init_sum_grad = init_sum_grad.add(&grad)?;

candle-core/src/cpu_backend/mod.rs

@@ -7,7 +7,7 @@ use rayon::prelude::*;
 
 mod utils;
 pub use utils::{
-    binary_map, binary_map_vec, unary_map, unary_map_vec, Map1, Map1Any, Map2, Map2U8,
+    binary_map, binary_map_vec, unary_map, unary_map_vec, Map1, Map1Any, Map2, Map2InPlace, Map2U8,
 };
 
 const USE_IM2COL_CONV1D: bool = true;
@@ -554,26 +554,65 @@ impl<I: IntDType> Map1 for IndexSelect<'_, I> {
     }
 }
 
-struct ScatterAdd<'a, I: IntDType> {
+trait ElemUpdate {
+    fn f<T: WithDType>(dst: &mut T, src: T);
+}
+
+struct Set;
+struct Add;
+
+impl ElemUpdate for Set {
+    fn f<T: WithDType>(dst: &mut T, src: T) {
+        *dst = src
+    }
+}
+
+impl ElemUpdate for Add {
+    fn f<T: WithDType>(dst: &mut T, src: T) {
+        *dst += src
+    }
+}
+
+struct Scatter<'a, I: IntDType, M: ElemUpdate> {
     ids: &'a [I],
     ids_l: &'a Layout,
     dim: usize,
+    _phantom: std::marker::PhantomData<M>,
 }
 
-impl<I: IntDType> Map2 for ScatterAdd<'_, I> {
-    const OP: &'static str = "scatter-add";
-    fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
-        let dst_len = l1.shape().elem_count();
-        let mut dst = vec![T::zero(); dst_len];
-        copy_strided_src_(v1, &mut dst, 0, l1);
+impl<'a, I: IntDType, M: ElemUpdate> Scatter<'a, I, M> {
+    fn new(ids: &'a [I], ids_l: &'a Layout, dim: usize) -> Self {
+        Self {
+            ids,
+            ids_l,
+            dim,
+            _phantom: Default::default(),
+        }
+    }
+}
+
+impl<I: IntDType, M: ElemUpdate> Map2InPlace for Scatter<'_, I, M> {
+    const OP: &'static str = "scatter";
+    fn f<T: WithDType>(
+        &self,
+        dst: &mut [T],
+        dst_l: &Layout,
+        src: &[T],
+        src_l: &Layout,
+    ) -> Result<()> {
+        let dst = match dst_l.contiguous_offsets() {
+            None => Err(Error::RequiresContiguous { op: "scatter" }.bt())?,
+            Some((o1, o2)) => &mut dst[o1..o2],
+        };
+
         let src = match src_l.contiguous_offsets() {
-            None => Err(Error::RequiresContiguous { op: "scatter-add" }.bt())?,
+            None => Err(Error::RequiresContiguous { op: "scatter" }.bt())?,
             Some((o1, o2)) => &src[o1..o2],
         };
 
         let dim = self.dim;
         let ids_dims = self.ids_l.dims();
-        let dst_dims = l1.dims();
+        let dst_dims = dst_l.dims();
         let dst_dim_len = dst_dims[dim];
         let dst_right_len: usize = dst_dims[dim + 1..].iter().product();
 
@@ -602,12 +641,12 @@ impl<I: IntDType> Map2 for ScatterAdd<'_, I> {
                         .bt())?
                     }
                     let dst_idx = start_dst_idx + index * dst_right_len + right_i;
-                    dst[dst_idx] += src[ids_idx]
+                    M::f(&mut dst[dst_idx], src[ids_idx])
                 }
             }
         }
 
-        Ok(dst)
+        Ok(())
     }
 }
 
@@ -2381,19 +2420,36 @@ impl BackendStorage for CpuStorage {
         }
     }
 
-    fn scatter_add(
-        &self,
+    fn scatter_set(
+        &mut self,
         l: &Layout,
         ids: &Self,
         ids_l: &Layout,
         src: &Self,
         src_l: &Layout,
         dim: usize,
-    ) -> Result<Self> {
+    ) -> Result<()> {
         match ids {
-            Self::U8(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
-            Self::U32(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
-            Self::I64(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
+            Self::U8(ids) => Scatter::<_, Set>::new(ids, ids_l, dim).map(self, l, src, src_l),
+            Self::U32(ids) => Scatter::<_, Set>::new(ids, ids_l, dim).map(self, l, src, src_l),
+            Self::I64(ids) => Scatter::<_, Set>::new(ids, ids_l, dim).map(self, l, src, src_l),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter").bt()),
+        }
+    }
+
+    fn scatter_add_set(
+        &mut self,
+        l: &Layout,
+        ids: &Self,
+        ids_l: &Layout,
+        src: &Self,
+        src_l: &Layout,
+        dim: usize,
+    ) -> Result<()> {
+        match ids {
+            Self::U8(ids) => Scatter::<_, Add>::new(ids, ids_l, dim).map(self, l, src, src_l),
+            Self::U32(ids) => Scatter::<_, Add>::new(ids, ids_l, dim).map(self, l, src, src_l),
+            Self::I64(ids) => Scatter::<_, Add>::new(ids, ids_l, dim).map(self, l, src, src_l),
             _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add").bt()),
         }
     }
@@ -2454,6 +2510,48 @@ impl BackendStorage for CpuStorage {
     fn to_cpu_storage(&self) -> Result<CpuStorage> {
         Ok(self.clone())
     }
+
+    fn const_set(&mut self, s: crate::scalar::Scalar, l: &Layout) -> Result<()> {
+        use crate::scalar::Scalar;
+        fn set<T: crate::WithDType>(src: &mut [T], l: &Layout, s: T) {
+            match l.strided_blocks() {
+                crate::StridedBlocks::SingleBlock { start_offset, len } => {
+                    src[start_offset..start_offset + len].fill(s)
+                }
+                crate::StridedBlocks::MultipleBlocks {
+                    block_start_index,
+                    block_len: 1,
+                } => {
+                    for src_index in block_start_index {
+                        src[src_index] = s
+                    }
+                }
+                crate::StridedBlocks::MultipleBlocks {
+                    block_start_index,
+                    block_len,
+                } => {
+                    for src_index in block_start_index {
+                        src[src_index..src_index + block_len].fill(s)
+                    }
+                }
+            }
+        }
+        match (self, s) {
+            (Self::BF16(storage), Scalar::BF16(v)) => set(storage, l, v),
+            (Self::F16(storage), Scalar::F16(v)) => set(storage, l, v),
+            (Self::F32(storage), Scalar::F32(v)) => set(storage, l, v),
+            (Self::F64(storage), Scalar::F64(v)) => set(storage, l, v),
+            (Self::U8(storage), Scalar::U8(v)) => set(storage, l, v),
+            (Self::U32(storage), Scalar::U32(v)) => set(storage, l, v),
+            (Self::I64(storage), Scalar::I64(v)) => set(storage, l, v),
+            (st, s) => crate::bail!(
+                "const_set dtype mismatch, expected {:?} but got {:?}",
+                st.dtype(),
+                s
+            ),
+        }
+        Ok(())
+    }
 }
 
 impl BackendDevice for CpuDevice {
@@ -2628,20 +2726,6 @@ impl BackendDevice for CpuDevice {
         Ok(storage)
     }
 
-    fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
-        let elem_count = shape.elem_count();
-        let storage = match dtype {
-            DType::U8 => CpuStorage::U8(vec![1u8; elem_count]),
-            DType::U32 => CpuStorage::U32(vec![1u32; elem_count]),
-            DType::I64 => CpuStorage::I64(vec![1i64; elem_count]),
-            DType::BF16 => CpuStorage::BF16(vec![bf16::ONE; elem_count]),
-            DType::F16 => CpuStorage::F16(vec![f16::ONE; elem_count]),
-            DType::F32 => CpuStorage::F32(vec![1f32; elem_count]),
-            DType::F64 => CpuStorage::F64(vec![1f64; elem_count]),
-        };
-        Ok(storage)
-    }
-
     fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
         let elem_count = shape.elem_count();
         let storage = match dtype {

candle-core/src/cpu_backend/utils.rs

@@ -58,6 +58,30 @@ pub trait Map2 {
     }
 }
 
+pub trait Map2InPlace {
+    const OP: &'static str;
+    fn f<T: WithDType>(&self, v1: &mut [T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<()>;
+
+    fn map(&self, v1: &mut C, l1: &Layout, v2: &C, l2: &Layout) -> Result<()> {
+        match (v1, v2) {
+            (C::U8(v1), C::U8(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::U32(v1), C::U32(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::I64(v1), C::I64(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::BF16(v1), C::BF16(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::F16(v1), C::F16(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::F32(v1), C::F32(v2)) => self.f(v1, l1, v2, l2)?,
+            (C::F64(v1), C::F64(v2)) => self.f(v1, l1, v2, l2)?,
+            (v1, v2) => Err(Error::DTypeMismatchBinaryOp {
+                lhs: v1.dtype(),
+                rhs: v2.dtype(),
+                op: Self::OP,
+            }
+            .bt())?,
+        };
+        Ok(())
+    }
+}
+
 pub trait Map2U8 {
     const OP: &'static str;
     fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<u8>>;

candle-core/src/cuda_backend/device.rs

@@ -2,7 +2,7 @@ use crate::backend::BackendDevice;
 use crate::{CpuStorage, CpuStorageRef, DType, Layout, Result, Shape};
 pub use candle_kernels as kernels;
 pub use cudarc;
-use cudarc::driver::{CudaFunction, LaunchConfig, PushKernelArg};
+use cudarc::driver::CudaFunction;
 use half::{bf16, f16};
 use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
@@ -188,100 +188,6 @@ impl CudaDevice {
         self.id
     }
 
-    fn const_impl(&self, v: f64, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
-        let elem_count = shape.elem_count();
-        let cfg = LaunchConfig::for_num_elems(elem_count as u32);
-        let slice = match dtype {
-            DType::U8 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<u8>(elem_count)? };
-                let func = self.get_or_load_func("fill_u8", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = v as u8;
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::U8(data)
-            }
-            DType::U32 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<u32>(elem_count)? };
-                let func = self.get_or_load_func("fill_u32", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = v as u32;
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::U32(data)
-            }
-            DType::I64 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<i64>(elem_count)? };
-                let func = self.get_or_load_func("fill_i64", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = v as i64;
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::I64(data)
-            }
-            DType::BF16 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<bf16>(elem_count)? };
-                let func = self.get_or_load_func("fill_bf16", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = bf16::from_f64(v);
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            DType::F16 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f16>(elem_count)? };
-                let func = self.get_or_load_func("fill_f16", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = f16::from_f64(v);
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::F16(data)
-            }
-            DType::F32 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f32>(elem_count)? };
-                let func = self.get_or_load_func("fill_f32", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                let v = v as f32;
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f64>(elem_count) }?;
-                let func = self.get_or_load_func("fill_f64", &kernels::FILL)?;
-                let mut builder = self.stream.launch_builder(&func);
-                builder.arg(&data);
-                builder.arg(&v);
-                builder.arg(&elem_count);
-                unsafe { builder.launch(cfg) }.w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
     pub fn get_or_load_custom_func(
         &self,
         fn_name: &str,
@@ -504,10 +410,6 @@ impl BackendDevice for CudaDevice {
         })
     }
 
-    fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
-        self.const_impl(1., shape, dtype)
-    }
-
     unsafe fn alloc_uninit(&self, shape: &Shape, dtype: DType) -> Result<Self::Storage> {
         let elem_count = shape.elem_count();
         let slice = match dtype {

candle-core/src/cuda_backend/mod.rs

@@ -2,7 +2,7 @@
 //!
 use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
-use crate::{builder_arg as barg, CpuStorage, DType, Layout, Result, Shape, WithDType};
+use crate::{builder_arg as barg, CpuStorage, DType, Layout, Result, WithDType};
 pub use candle_kernels as kernels;
 pub use cudarc;
 use cudarc::cublas::{Gemm, GemmConfig, StridedBatchedConfig};
@@ -34,6 +34,21 @@ impl<T: DeviceRepr> SlicePtrOrNull<T> {
     }
 }
 
+impl crate::scalar::Scalar {
+    pub fn builder_arg<'a, 'b: 'a>(&'b self, builder: &mut cudarc::driver::LaunchArgs<'a>) {
+        use crate::scalar::Scalar;
+        match self {
+            Scalar::U8(v) => builder.arg(v),
+            Scalar::U32(v) => builder.arg(v),
+            Scalar::I64(v) => builder.arg(v),
+            Scalar::F32(v) => builder.arg(v),
+            Scalar::F64(v) => builder.arg(v),
+            Scalar::F16(v) => builder.arg(v),
+            Scalar::BF16(v) => builder.arg(v),
+        };
+    }
+}
+
 impl SlicePtrOrNull<usize> {
     pub fn params_from_layout(dev: &CudaDevice, l: &Layout) -> Result<Self> {
         let ds = if l.is_contiguous() {
@@ -395,7 +410,7 @@ impl Map1 for IndexSelect<'_> {
             CudaStorageSlice::U8(slice) => ("is_u8", slice_ptr(slice, ids_l.start_offset())),
             CudaStorageSlice::I64(slice) => ("is_i64", slice_ptr(slice, ids_l.start_offset())),
             _ => Err(CudaError::UnexpectedDType {
-                msg: "index_select ids should be u8 or u32",
+                msg: "index_select ids should be u8, u32, or i64",
                 expected: DType::U32,
                 got: self.0.dtype(),
             })
@@ -492,7 +507,7 @@ impl Map2InPlace for IndexAdd<'_> {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         dst: &mut CudaSlice<T>,
-        dst_shape: &Shape,
+        dst_l: &Layout,
         src: &CudaSlice<T>,
         src_l: &Layout,
         dev: &CudaDevice,
@@ -514,6 +529,10 @@ impl Map2InPlace for IndexAdd<'_> {
                 got: ids.dtype(),
             })?,
         };
+        let dst = match dst_l.contiguous_offsets() {
+            Some((o1, o2)) => dst.slice(o1..o2),
+            None => Err(crate::Error::RequiresContiguous { op: "index-add" }.bt())?,
+        };
         let src = match src_l.contiguous_offsets() {
             Some((o1, o2)) => src.slice(o1..o2),
             None => Err(crate::Error::RequiresContiguous { op: "index-add" }.bt())?,
@@ -521,7 +540,7 @@ impl Map2InPlace for IndexAdd<'_> {
         let left_sz: usize = src_l.dims()[..dim].iter().product();
         let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
         let src_dim_sz = src_l.dims()[dim];
-        let dst_dim_sz = dst_shape.dims()[dim];
+        let dst_dim_sz = dst_l.dims()[dim];
         let ids_dim_sz = ids_l.dims()[0];
         let cfg = LaunchConfig::for_num_elems((left_sz * right_sz) as u32);
         let func = dev.get_or_load_func(&kernel_name::<T>(name), &kernels::INDEXING)?;
@@ -529,7 +548,59 @@ impl Map2InPlace for IndexAdd<'_> {
         barg!(builder, ids);
         barg!(builder, ids_dim_sz);
         builder.arg(&src);
-        builder.arg(dst);
+        builder.arg(&dst);
+        barg!(builder, left_sz, src_dim_sz, dst_dim_sz, right_sz);
+        // SAFETY: ffi.
+        unsafe { builder.launch(cfg) }.w()?;
+        Ok(())
+    }
+}
+
+struct Scatter<'a>(&'a CudaStorage, &'a Layout, usize);
+impl Map2InPlace for Scatter<'_> {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        dst: &mut CudaSlice<T>,
+        dst_l: &Layout,
+        src: &CudaSlice<T>,
+        src_l: &Layout,
+        dev: &CudaDevice,
+    ) -> Result<()> {
+        let ids = &self.0;
+        let ids_l = &self.1;
+        let dim = self.2;
+        let (ids_o1, _) = match ids_l.contiguous_offsets() {
+            Some(o12) => o12,
+            None => Err(crate::Error::RequiresContiguous { op: "scatter" }.bt())?,
+        };
+        let (name, (ids, _guard)) = match &ids.slice {
+            CudaStorageSlice::U32(slice) => ("s_u32", slice_ptr(slice, ids_o1)),
+            CudaStorageSlice::I64(slice) => ("s_i64", slice_ptr(slice, ids_o1)),
+            CudaStorageSlice::U8(slice) => ("s_u8", slice_ptr(slice, ids_o1)),
+            _ => Err(CudaError::UnexpectedDType {
+                msg: "scatter ids should be u8/u32/i64",
+                expected: DType::U32,
+                got: ids.dtype(),
+            })?,
+        };
+        let dst = match dst_l.contiguous_offsets() {
+            Some((o1, o2)) => dst.slice(o1..o2),
+            None => Err(crate::Error::RequiresContiguous { op: "scatter" }.bt())?,
+        };
+        let src = match src_l.contiguous_offsets() {
+            Some((o1, o2)) => src.slice(o1..o2),
+            None => Err(crate::Error::RequiresContiguous { op: "scatter" }.bt())?,
+        };
+        let left_sz: usize = src_l.dims()[..dim].iter().product();
+        let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
+        let src_dim_sz = src_l.dims()[dim];
+        let dst_dim_sz = dst_l.dims()[dim];
+        let cfg = LaunchConfig::for_num_elems((left_sz * right_sz) as u32);
+        let func = dev.get_or_load_func(&kernel_name::<T>(name), &kernels::INDEXING)?;
+        let mut builder = func.builder();
+        barg!(builder, ids);
+        builder.arg(&src);
+        builder.arg(&dst);
         barg!(builder, left_sz, src_dim_sz, dst_dim_sz, right_sz);
         // SAFETY: ffi.
         unsafe { builder.launch(cfg) }.w()?;
@@ -542,7 +613,7 @@ impl Map2InPlace for ScatterAdd<'_> {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         dst: &mut CudaSlice<T>,
-        dst_shape: &Shape,
+        dst_l: &Layout,
         src: &CudaSlice<T>,
         src_l: &Layout,
         dev: &CudaDevice,
@@ -564,6 +635,10 @@ impl Map2InPlace for ScatterAdd<'_> {
                 got: ids.dtype(),
             })?,
         };
+        let dst = match dst_l.contiguous_offsets() {
+            Some((o1, o2)) => dst.slice(o1..o2),
+            None => Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt())?,
+        };
         let src = match src_l.contiguous_offsets() {
             Some((o1, o2)) => src.slice(o1..o2),
             None => Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt())?,
@@ -571,13 +646,13 @@ impl Map2InPlace for ScatterAdd<'_> {
         let left_sz: usize = src_l.dims()[..dim].iter().product();
         let right_sz: usize = src_l.dims()[dim + 1..].iter().product();
         let src_dim_sz = src_l.dims()[dim];
-        let dst_dim_sz = dst_shape.dims()[dim];
+        let dst_dim_sz = dst_l.dims()[dim];
         let cfg = LaunchConfig::for_num_elems((left_sz * right_sz) as u32);
         let func = dev.get_or_load_func(&kernel_name::<T>(name), &kernels::INDEXING)?;
         let mut builder = func.builder();
         barg!(builder, ids);
         builder.arg(&src);
-        builder.arg(dst);
+        builder.arg(&dst);
         barg!(builder, left_sz, src_dim_sz, dst_dim_sz, right_sz);
         // SAFETY: ffi.
         unsafe { builder.launch(cfg) }.w()?;
@@ -1235,6 +1310,36 @@ impl BackendStorage for CudaStorage {
         &self.device
     }
 
+    fn const_set(&mut self, s: crate::scalar::Scalar, layout: &Layout) -> Result<()> {
+        let dev = &self.device;
+        let shape = layout.shape();
+        let dims = shape.dims();
+        let el_count = shape.elem_count();
+        let cfg = LaunchConfig::for_num_elems(el_count as u32);
+        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        let src_o = layout.start_offset();
+        let ((src, _guard_src), kernel_name) = match &mut self.slice {
+            S::U8(s) => (slice_ptr(s, src_o), "const_set_u8"),
+            S::U32(s) => (slice_ptr(s, src_o), "const_set_u32"),
+            S::I64(s) => (slice_ptr(s, src_o), "const_set_i64"),
+            S::BF16(s) => (slice_ptr(s, src_o), "const_set_bf16"),
+            S::F16(s) => (slice_ptr(s, src_o), "const_set_f16"),
+            S::F32(s) => (slice_ptr(s, src_o), "const_set_f32"),
+            S::F64(s) => (slice_ptr(s, src_o), "const_set_f64"),
+        };
+
+        let func = dev.get_or_load_func(kernel_name, &kernels::FILL)?;
+        let mut builder = func.builder();
+        barg!(builder, el_count);
+        barg!(builder, dims.len());
+        ds.builder_arg(&mut builder);
+        s.builder_arg(&mut builder);
+        barg!(builder, src);
+        // SAFETY: ffi.
+        unsafe { builder.launch(cfg) }.w()?;
+        Ok(())
+    }
+
     fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
         let shape = layout.shape();
         let dims = shape.dims();
@@ -1793,20 +1898,29 @@ impl BackendStorage for CudaStorage {
         let slice = Gather(ids, ids_l, dim).map(&self.slice, &device, l)?;
         Ok(Self { slice, device })
     }
-    fn scatter_add(
-        &self,
+    fn scatter_set(
+        &mut self,
         l: &Layout,
         ids: &Self,
         ids_l: &Layout,
         src: &Self,
         src_l: &Layout,
         dim: usize,
-    ) -> Result<Self> {
+    ) -> Result<()> {
         let device = self.device().clone();
-        let mut acc = unsafe { device.alloc_uninit(l.shape(), self.dtype())? };
-        self.copy_strided_src(&mut acc, 0, l)?;
-        ScatterAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
-        Ok(acc)
+        Scatter(ids, ids_l, dim).map(&mut self.slice, l, &src.slice, src_l, &device)
+    }
+    fn scatter_add_set(
+        &mut self,
+        l: &Layout,
+        ids: &Self,
+        ids_l: &Layout,
+        src: &Self,
+        src_l: &Layout,
+        dim: usize,
+    ) -> Result<()> {
+        let device = self.device().clone();
+        ScatterAdd(ids, ids_l, dim).map(&mut self.slice, l, &src.slice, src_l, &device)
     }
     fn index_add(
         &self,
@@ -1820,7 +1934,7 @@ impl BackendStorage for CudaStorage {
         let device = self.device().clone();
         let mut acc = unsafe { device.alloc_uninit(l.shape(), self.dtype())? };
         self.copy_strided_src(&mut acc, 0, l)?;
-        IndexAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
+        IndexAdd(ids, ids_l, dim).map(&mut acc.slice, l, &src.slice, src_l, &device)?;
         Ok(acc)
     }
 

candle-core/src/cuda_backend/utils.rs

@@ -1,5 +1,5 @@
 /// Helper functions to plug cuda kernels in candle.
-use crate::{Layout, Result, Shape, WithDType};
+use crate::{Layout, Result, WithDType};
 pub use cudarc;
 use cudarc::driver::{CudaSlice, DeviceRepr, ValidAsZeroBits};
 
@@ -96,7 +96,7 @@ pub trait Map2InPlace {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         dst: &mut CudaSlice<T>,
-        dst_shape: &Shape,
+        dst_l: &Layout,
         src: &CudaSlice<T>,
         src_l: &Layout,
         dev: &CudaDevice,
@@ -105,19 +105,19 @@ pub trait Map2InPlace {
     fn map(
         &self,
         dst: &mut S,
-        dst_s: &Shape,
+        dst_l: &Layout,
         src: &S,
         src_l: &Layout,
         d: &CudaDevice,
     ) -> Result<()> {
         match (dst, src) {
-            (S::U8(dst), S::U8(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::U32(dst), S::U32(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::I64(dst), S::I64(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::BF16(dst), S::BF16(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F16(dst), S::F16(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F32(dst), S::F32(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F64(dst), S::F64(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::U8(dst), S::U8(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::U32(dst), S::U32(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::I64(dst), S::I64(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::BF16(dst), S::BF16(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::F16(dst), S::F16(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::F32(dst), S::F32(src)) => self.f(dst, dst_l, src, src_l, d),
+            (S::F64(dst), S::F64(src)) => self.f(dst, dst_l, src, src_l, d),
             _ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
         }
     }

candle-core/src/device.rs

@@ -292,23 +292,6 @@ impl Device {
         self.rand_normal_f64(mean.to_f64(), std.to_f64(), shape, T::DTYPE)
     }
 
-    pub(crate) fn ones(&self, shape: &Shape, dtype: DType) -> Result<Storage> {
-        match self {
-            Device::Cpu => {
-                let storage = CpuDevice.ones_impl(shape, dtype)?;
-                Ok(Storage::Cpu(storage))
-            }
-            Device::Cuda(device) => {
-                let storage = device.ones_impl(shape, dtype)?;
-                Ok(Storage::Cuda(storage))
-            }
-            Device::Metal(device) => {
-                let storage = device.ones_impl(shape, dtype)?;
-                Ok(Storage::Metal(storage))
-            }
-        }
-    }
-
     pub(crate) fn zeros(&self, shape: &Shape, dtype: DType) -> Result<Storage> {
         match self {
             Device::Cpu => {

candle-core/src/dtype.rs

@@ -107,6 +107,7 @@ pub trait WithDType:
 
     fn from_f64(v: f64) -> Self;
     fn to_f64(self) -> f64;
+    fn to_scalar(self) -> crate::scalar::Scalar;
     fn cpu_storage_ref(data: &[Self]) -> CpuStorageRef<'_>;
     fn to_cpu_storage_owned(data: Vec<Self>) -> CpuStorage;
 
@@ -131,6 +132,10 @@ macro_rules! with_dtype {
                 $to_f64(self)
             }
 
+            fn to_scalar(self) -> crate::scalar::Scalar {
+                crate::scalar::Scalar::$dtype(self)
+            }
+
             fn cpu_storage_ref(data: &[Self]) -> CpuStorageRef<'_> {
                 CpuStorageRef::$dtype(data)
             }

candle-core/src/dummy_cuda_backend.rs

@@ -37,6 +37,10 @@ impl crate::backend::BackendStorage for CudaStorage {
         fail!()
     }
 
+    fn const_set(&mut self, _: crate::scalar::Scalar, _: &Layout) -> Result<()> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn to_cpu_storage(&self) -> Result<CpuStorage> {
         Err(Error::NotCompiledWithCudaSupport)
     }
@@ -124,15 +128,27 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn scatter_add(
-        &self,
+    fn scatter_set(
+        &mut self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: usize,
-    ) -> Result<Self> {
+    ) -> Result<()> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
+    fn scatter_add_set(
+        &mut self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: usize,
+    ) -> Result<()> {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
@@ -214,10 +230,6 @@ impl crate::backend::BackendDevice for CudaDevice {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
     unsafe fn alloc_uninit(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithCudaSupport)
     }

candle-core/src/dummy_metal_backend.rs

@@ -41,6 +41,10 @@ impl crate::backend::BackendStorage for MetalStorage {
         fail!()
     }
 
+    fn const_set(&mut self, _: crate::scalar::Scalar, _: &Layout) -> Result<()> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
     fn to_cpu_storage(&self) -> Result<CpuStorage> {
         Err(Error::NotCompiledWithMetalSupport)
     }
@@ -128,15 +132,27 @@ impl crate::backend::BackendStorage for MetalStorage {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
-    fn scatter_add(
-        &self,
+    fn scatter_set(
+        &mut self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: &Self,
         _: &Layout,
         _: usize,
-    ) -> Result<Self> {
+    ) -> Result<()> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn scatter_add_set(
+        &mut self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: usize,
+    ) -> Result<()> {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
@@ -218,10 +234,6 @@ impl crate::backend::BackendDevice for MetalDevice {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
-    fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
-        Err(Error::NotCompiledWithMetalSupport)
-    }
-
     unsafe fn alloc_uninit(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithMetalSupport)
     }

candle-core/src/metal_backend/mod.rs

@@ -413,6 +413,100 @@ impl BackendStorage for MetalStorage {
         self.binary(name, rhs, lhs_l, rhs_l)
     }
 
+    fn const_set(&mut self, s: crate::scalar::Scalar, l: &Layout) -> Result<()> {
+        use crate::scalar::Scalar;
+        fn set<S: crate::WithDType + candle_metal_kernels::utils::EncoderParam>(
+            self_: &mut MetalStorage,
+            s: S,
+            l: &Layout,
+        ) -> Result<()> {
+            let device = self_.device();
+            let dtype = self_.dtype;
+            let shape = l.shape();
+            let el_count = shape.elem_count();
+            let command_buffer = device.command_buffer()?;
+            command_buffer.set_label("const-set");
+            let dst = buffer_o(&self_.buffer, l, self_.dtype);
+
+            match (el_count % 2, dtype, l.is_contiguous()) {
+                (0, DType::BF16 | DType::F16, true) => {
+                    use candle_metal_kernels::unary::contiguous_tiled;
+                    let kernel_name = match dtype {
+                        DType::F16 => contiguous_tiled::const_set::HALF,
+                        DType::BF16 => contiguous_tiled::const_set::BFLOAT,
+                        _ => crate::bail!("internal bug in const_set"),
+                    };
+                    candle_metal_kernels::call_const_set_contiguous_tiled(
+                        &device.device,
+                        &command_buffer,
+                        &device.kernels,
+                        kernel_name,
+                        el_count,
+                        s,
+                        dst,
+                    )
+                    .map_err(MetalError::from)?;
+                }
+                (_, _, true) => {
+                    use candle_metal_kernels::unary::contiguous;
+                    let kernel_name = match dtype {
+                        DType::F16 => contiguous::const_set::HALF,
+                        DType::BF16 => contiguous::const_set::BFLOAT,
+                        DType::F32 => contiguous::const_set::FLOAT,
+                        DType::I64 => contiguous::const_set::I64,
+                        DType::U32 => contiguous::const_set::U32,
+                        DType::U8 => contiguous::const_set::U8,
+                        DType::F64 => crate::bail!("unsupported const-set f64"),
+                    };
+                    candle_metal_kernels::call_const_set_contiguous(
+                        &device.device,
+                        &command_buffer,
+                        &device.kernels,
+                        kernel_name,
+                        el_count,
+                        s,
+                        dst,
+                    )
+                    .map_err(MetalError::from)?;
+                }
+                (_, _, false) => {
+                    use candle_metal_kernels::unary::strided;
+                    let kernel_name = match dtype {
+                        DType::F16 => strided::const_set::HALF,
+                        DType::BF16 => strided::const_set::BFLOAT,
+                        DType::F32 => strided::const_set::FLOAT,
+                        DType::I64 => strided::const_set::I64,
+                        DType::U32 => strided::const_set::U32,
+                        DType::U8 => strided::const_set::U8,
+                        DType::F64 => crate::bail!("unsupported const-set f64"),
+                    };
+                    candle_metal_kernels::call_const_set_strided(
+                        &device.device,
+                        &command_buffer,
+                        &device.kernels,
+                        kernel_name,
+                        l.dims(),
+                        s,
+                        l.stride(),
+                        dst,
+                    )
+                    .map_err(MetalError::from)?;
+                }
+            }
+            Ok(())
+        }
+        match (self.dtype, s) {
+            (DType::U8, Scalar::U8(s)) => set(self, s, l),
+            (DType::U32, Scalar::U32(s)) => set(self, s, l),
+            (DType::I64, Scalar::I64(s)) => set(self, s, l),
+            (DType::F16, Scalar::F16(s)) => set(self, s, l),
+            (DType::BF16, Scalar::BF16(s)) => set(self, s, l),
+            (DType::F32, Scalar::F32(s)) => set(self, s, l),
+            (DType::F64, Scalar::F64(s)) => set(self, s, l),
+            _ => crate::bail!("dtype mismatch, expected {:?}, got {:?}", self.dtype, s),
+        }
+    }
+
     fn to_dtype(&self, layout: &Layout, dtype: DType) -> Result<Self> {
         let device = self.device();
         let shape = layout.shape();
@@ -1332,18 +1426,65 @@ impl BackendStorage for MetalStorage {
         Ok(Self::new(buffer, device.clone(), dst_el, dtype))
     }
 
-    fn scatter_add(
-        &self,
+    fn scatter_set(
+        &mut self,
         l: &Layout,
         ids: &Self,
         ids_l: &Layout,
         src: &Self,
         src_l: &Layout,
         dim: usize,
-    ) -> Result<Self> {
-        let mut acc = self.device.zeros_impl(l.shape(), self.dtype())?;
-        self.copy_strided_src(&mut acc, 0, l)?;
-        if !ids_l.is_contiguous() || !src_l.is_contiguous() {
+    ) -> Result<()> {
+        if !l.is_contiguous() || !ids_l.is_contiguous() || !src_l.is_contiguous() {
+            return Err(crate::Error::RequiresContiguous { op: "scatter" }.bt());
+        };
+        let name = match (ids.dtype, self.dtype) {
+            (DType::U8, DType::F32) => "s_u8_f32",
+            (DType::U8, DType::F16) => "s_u8_f16",
+            (DType::U8, DType::BF16) => "s_u8_bf16",
+            (DType::U32, DType::U32) => "s_u32_u32",
+            (DType::U32, DType::F32) => "s_u32_f32",
+            (DType::U32, DType::F16) => "s_u32_f16",
+            (DType::U32, DType::BF16) => "s_u32_bf16",
+            (DType::I64, DType::F32) => "s_i64_f32",
+            (DType::I64, DType::F16) => "s_i64_f16",
+            (DType::I64, DType::BF16) => "s_i64_bf16",
+            _ => Err(MetalError::UnexpectedDType {
+                msg: "scatter ids should be u8/u32/i64",
+                expected: DType::U32,
+                got: ids.dtype(),
+            })?,
+        };
+        let command_buffer = self.device.command_buffer()?;
+        let dst = buffer_o(&self.buffer, l, self.dtype);
+        let src = buffer_o(&src.buffer, src_l, src.dtype);
+        let ids = buffer_o(&ids.buffer, ids_l, ids.dtype);
+        candle_metal_kernels::call_scatter(
+            &self.device.device,
+            &command_buffer,
+            &self.device.kernels,
+            name,
+            src_l.dims(),
+            l.dims(),
+            dim,
+            src,
+            ids,
+            dst,
+        )
+        .map_err(MetalError::from)?;
+        Ok(())
+    }
+
+    fn scatter_add_set(
+        &mut self,
+        l: &Layout,
+        ids: &Self,
+        ids_l: &Layout,
+        src: &Self,
+        src_l: &Layout,
+        dim: usize,
+    ) -> Result<()> {
+        if !l.is_contiguous() || !ids_l.is_contiguous() || !src_l.is_contiguous() {
             return Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt());
         };
         let name = match (ids.dtype, self.dtype) {
@@ -1364,9 +1505,10 @@ impl BackendStorage for MetalStorage {
             })?,
         };
         let command_buffer = self.device.command_buffer()?;
+        let dst = buffer_o(&self.buffer, l, self.dtype);
         let src = buffer_o(&src.buffer, src_l, src.dtype);
         let ids = buffer_o(&ids.buffer, ids_l, ids.dtype);
-        candle_metal_kernels::call_scatter_add(
+        candle_metal_kernels::call_scatter(
             &self.device.device,
             &command_buffer,
             &self.device.kernels,
@@ -1376,10 +1518,10 @@ impl BackendStorage for MetalStorage {
             dim,
             src,
             ids,
-            &acc.buffer,
+            dst,
         )
         .map_err(MetalError::from)?;
-        Ok(acc)
+        Ok(())
     }
 
     fn index_select(&self, ids: &Self, src_l: &Layout, ids_l: &Layout, dim: usize) -> Result<Self> {
@@ -1965,40 +2107,6 @@ impl BackendDevice for MetalDevice {
         ))
     }
 
-    fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<MetalStorage> {
-        let name = match dtype {
-            DType::U8 => "fill_u8",
-            DType::U32 => "fill_u32",
-            DType::I64 => "fill_i64",
-            DType::F16 => "fill_f16",
-            DType::BF16 => "fill_bf16",
-            DType::F32 => "fill_f32",
-            DType::F64 => {
-                let cpu_storage = crate::cpu_backend::CpuDevice.ones_impl(shape, dtype)?;
-                return self.storage_from_cpu_storage(&cpu_storage);
-            }
-        };
-        let buffer = self.new_buffer(shape.elem_count(), dtype, "alloc-ones")?;
-        let command_buffer = self.command_buffer()?;
-        candle_metal_kernels::call_const_fill(
-            &self.device,
-            &command_buffer,
-            &self.kernels,
-            name,
-            shape.elem_count(),
-            &buffer,
-            1.,
-        )
-        .map_err(MetalError::from)?;
-
-        Ok(MetalStorage::new(
-            buffer,
-            self.clone(),
-            shape.elem_count(),
-            dtype,
-        ))
-    }
-
     fn storage_from_slice<T: crate::WithDType>(&self, s: &[T]) -> Result<Self::Storage> {
         let (count, buffer) = match T::cpu_storage_ref(s) {
             CpuStorageRef::U8(storage) => (storage.len(), self.new_buffer_with_data(storage)),

candle-core/src/op.rs

@@ -80,6 +80,7 @@ pub enum Op {
     Reduce(Tensor, ReduceOp, Vec<usize>),
     Matmul(Tensor, Tensor),
     Gather(Tensor, Tensor, usize),
+    Scatter(Tensor, Tensor, Tensor, usize),
     ScatterAdd(Tensor, Tensor, Tensor, usize),
     IndexSelect(Tensor, Tensor, usize),
     IndexAdd(Tensor, Tensor, Tensor, usize),

candle-core/src/scalar.rs

@@ -1,6 +1,74 @@
 //! TensorScalar Enum and Trait
 //!
-use crate::{Result, Tensor, WithDType};
+use crate::{DType, Result, Tensor, WithDType};
+use half::{bf16, f16};
+
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub enum Scalar {
+    U8(u8),
+    U32(u32),
+    I64(i64),
+    BF16(bf16),
+    F16(f16),
+    F32(f32),
+    F64(f64),
+}
+
+impl<T: WithDType> From<T> for Scalar {
+    fn from(value: T) -> Self {
+        value.to_scalar()
+    }
+}
+
+impl Scalar {
+    pub fn zero(dtype: DType) -> Self {
+        match dtype {
+            DType::U8 => Scalar::U8(0),
+            DType::U32 => Scalar::U32(0),
+            DType::I64 => Scalar::I64(0),
+            DType::BF16 => Scalar::BF16(bf16::ZERO),
+            DType::F16 => Scalar::F16(f16::ZERO),
+            DType::F32 => Scalar::F32(0.0),
+            DType::F64 => Scalar::F64(0.0),
+        }
+    }
+
+    pub fn one(dtype: DType) -> Self {
+        match dtype {
+            DType::U8 => Scalar::U8(1),
+            DType::U32 => Scalar::U32(1),
+            DType::I64 => Scalar::I64(1),
+            DType::BF16 => Scalar::BF16(bf16::ONE),
+            DType::F16 => Scalar::F16(f16::ONE),
+            DType::F32 => Scalar::F32(1.0),
+            DType::F64 => Scalar::F64(1.0),
+        }
+    }
+
+    pub fn dtype(&self) -> DType {
+        match self {
+            Scalar::U8(_) => DType::U8,
+            Scalar::U32(_) => DType::U32,
+            Scalar::I64(_) => DType::I64,
+            Scalar::BF16(_) => DType::BF16,
+            Scalar::F16(_) => DType::F16,
+            Scalar::F32(_) => DType::F32,
+            Scalar::F64(_) => DType::F64,
+        }
+    }
+
+    pub fn to_f64(&self) -> f64 {
+        match self {
+            Scalar::U8(v) => *v as f64,
+            Scalar::U32(v) => *v as f64,
+            Scalar::I64(v) => *v as f64,
+            Scalar::BF16(v) => v.to_f64(),
+            Scalar::F16(v) => v.to_f64(),
+            Scalar::F32(v) => *v as f64,
+            Scalar::F64(v) => *v,
+        }
+    }
+}
 
 pub enum TensorScalar {
     Tensor(Tensor),

candle-core/src/storage.rs

@@ -1,5 +1,6 @@
 use crate::backend::BackendStorage;
 use crate::op::{self, CmpOp, ReduceOp};
+use crate::scalar::Scalar;
 use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
 use crate::{CustomOp1, CustomOp2, CustomOp3, InplaceOp1, InplaceOp2, InplaceOp3};
 
@@ -73,6 +74,14 @@ impl Storage {
         }
     }
 
+    pub(crate) fn const_set(&mut self, v: Scalar, l: &Layout) -> Result<()> {
+        match self {
+            Storage::Cpu(storage) => storage.const_set(v, l),
+            Storage::Cuda(storage) => storage.const_set(v, l),
+            Storage::Metal(storage) => storage.const_set(v, l),
+        }
+    }
+
     pub(crate) fn affine(&self, layout: &Layout, mul: f64, add: f64) -> Result<Self> {
         match self {
             Storage::Cpu(storage) => {
@@ -619,32 +628,56 @@ impl Storage {
         }
     }
 
-    pub(crate) fn scatter_add(
-        &self,
+    pub(crate) fn scatter_set(
+        &mut self,
         l: &Layout,
         indexes: &Self,
         indexes_l: &Layout,
         source: &Self,
         source_l: &Layout,
         d: usize,
-    ) -> Result<Self> {
+    ) -> Result<()> {
+        self.same_device(indexes, "scatter-set")?;
+        self.same_device(source, "scatter-set")?;
+        match (self, indexes, source) {
+            (Self::Cpu(s), Self::Cpu(indexes), Self::Cpu(source)) => {
+                s.scatter_set(l, indexes, indexes_l, source, source_l, d)?;
+            }
+            (Self::Cuda(s), Self::Cuda(indexes), Self::Cuda(source)) => {
+                s.scatter_set(l, indexes, indexes_l, source, source_l, d)?;
+            }
+            (Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
+                s.scatter_set(l, indexes, indexes_l, source, source_l, d)?;
+            }
+            _ => unreachable!(),
+        }
+        Ok(())
+    }
+
+    pub(crate) fn scatter_add(
+        &mut self,
+        l: &Layout,
+        indexes: &Self,
+        indexes_l: &Layout,
+        source: &Self,
+        source_l: &Layout,
+        d: usize,
+    ) -> Result<()> {
         self.same_device(indexes, "scatter-add")?;
         self.same_device(source, "scatter-add")?;
         match (self, indexes, source) {
             (Self::Cpu(s), Self::Cpu(indexes), Self::Cpu(source)) => {
-                let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
-                Ok(Self::Cpu(storage))
+                s.scatter_add_set(l, indexes, indexes_l, source, source_l, d)?;
             }
             (Self::Cuda(s), Self::Cuda(indexes), Self::Cuda(source)) => {
-                let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
-                Ok(Self::Cuda(storage))
+                s.scatter_add_set(l, indexes, indexes_l, source, source_l, d)?;
             }
             (Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
-                let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
-                Ok(Self::Metal(storage))
+                s.scatter_add_set(l, indexes, indexes_l, source, source_l, d)?;
             }
             _ => unreachable!(),
         }
+        Ok(())
     }
 
     pub(crate) fn index_add(

candle-core/src/tensor.rs

@@ -3,7 +3,7 @@
 use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{BackpropOp, BinaryOp, CmpOp, Op, ReduceOp, UnaryOp};
 use crate::scalar::TensorOrScalar;
-use crate::shape::{Dim, Dims};
+use crate::shape::{Dim, Dims, ShapeWithOneHole};
 use crate::{bail, storage::Storage, DType, Device, Error, Layout, Result, Shape};
 use std::sync::{Arc, RwLock};
 
@@ -185,7 +185,9 @@ impl Tensor {
     ) -> Result<Self> {
         let none = BackpropOp::none();
         let shape = shape.into();
-        let storage = device.ones(&shape, dtype)?;
+        let mut storage = unsafe { device.alloc_uninit(&shape, dtype)? };
+        let layout = Layout::contiguous(shape.clone());
+        storage.const_set(crate::scalar::Scalar::one(dtype), &layout)?;
         Ok(from_storage(storage, shape, none, is_variable))
     }
 
@@ -202,6 +204,18 @@ impl Tensor {
         Self::ones_impl(shape, dtype, device, false)
     }
 
+    pub fn const_set(&self, value: crate::scalar::Scalar) -> Result<()> {
+        self.storage_mut().const_set(value, self.layout())
+    }
+
+    pub fn zero_set(&self) -> Result<()> {
+        self.const_set(crate::scalar::Scalar::zero(self.dtype()))
+    }
+
+    pub fn one_set(&self) -> Result<()> {
+        self.const_set(crate::scalar::Scalar::one(self.dtype()))
+    }
+
     /// Creates a new tensor filled with ones with same shape, dtype, and device as the other tensor.
     ///
     /// ```rust
@@ -452,17 +466,13 @@ impl Tensor {
         Self::from_vec_impl(data, len, device, false)
     }
 
-    pub(crate) fn from_vec_impl<S: Into<Shape>, D: crate::WithDType>(
+    pub(crate) fn from_vec_impl<S: ShapeWithOneHole, D: crate::WithDType>(
         data: Vec<D>,
         shape: S,
         device: &Device,
         is_variable: bool,
     ) -> Result<Self> {
-        let shape = shape.into();
-        let buffer_size = data.len();
-        if buffer_size != shape.elem_count() {
-            return Err(Error::ShapeMismatch { buffer_size, shape }.bt());
-        }
+        let shape = shape.into_shape(data.len())?;
         let storage = device.storage_owned(data)?;
         let none = BackpropOp::none();
         Ok(from_storage(storage, shape, none, is_variable))
@@ -481,7 +491,7 @@ impl Tensor {
     /// ]);
     /// # Ok::<(), candle_core::Error>(())
     /// ```
-    pub fn from_vec<S: Into<Shape>, D: crate::WithDType>(
+    pub fn from_vec<S: ShapeWithOneHole, D: crate::WithDType>(
         data: Vec<D>,
         shape: S,
         device: &Device,
@@ -502,17 +512,12 @@ impl Tensor {
     /// ]);
     /// # Ok::<(), candle_core::Error>(())
     /// ```
-    pub fn from_slice<S: Into<Shape>, D: crate::WithDType>(
+    pub fn from_slice<S: ShapeWithOneHole, D: crate::WithDType>(
         array: &[D],
         shape: S,
         device: &Device,
     ) -> Result<Self> {
-        let shape = shape.into();
-        let n: usize = shape.elem_count();
-        let buffer_size: usize = array.len();
-        if buffer_size != n {
-            return Err(Error::ShapeMismatch { buffer_size, shape }.bt());
-        }
+        let shape = shape.into_shape(array.len())?;
         let storage = device.storage_from_slice(array)?;
         let none = BackpropOp::none();
         Ok(from_storage(storage, shape, none, false))
@@ -1349,8 +1354,7 @@ impl Tensor {
         self.index_select(ids, 0)
     }
 
-    pub fn scatter_add<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
-        let dim = dim.to_index(self.shape(), "scatter-add")?;
+    fn scatter_checks(&self, indexes: &Self, source: &Self, dim: usize) -> Result<()> {
         let source_dims = source.dims();
         let self_dims = self.dims();
         let mismatch = if source_dims.len() != self_dims.len() {
@@ -1367,7 +1371,7 @@ impl Tensor {
         };
         if mismatch {
             Err(Error::ShapeMismatchBinaryOp {
-                op: "scatter-add (self, src)",
+                op: "scatter (self, src)",
                 lhs: self.shape().clone(),
                 rhs: source.shape().clone(),
             }
@@ -1375,13 +1379,44 @@ impl Tensor {
         }
         if indexes.dims() != source.dims() {
             Err(Error::ShapeMismatchBinaryOp {
-                op: "scatter-add (indexes, src)",
+                op: "scatter (indexes, src)",
                 lhs: indexes.shape().clone(),
                 rhs: source.shape().clone(),
             }
             .bt())?
         }
-        let storage = self.storage().scatter_add(
+        Ok(())
+    }
+
+    pub fn scatter<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "scatter")?;
+        self.scatter_checks(indexes, source, dim)?;
+        let shape = self.shape();
+        let mut storage = unsafe { self.device().alloc_uninit(shape, self.dtype())? };
+        self.storage()
+            .copy_strided_src(&mut storage, 0, self.layout())?;
+        let layout = Layout::contiguous(shape);
+        storage.scatter_set(
+            &layout,
+            &indexes.storage(),
+            indexes.layout(),
+            &source.storage(),
+            source.layout(),
+            dim,
+        )?;
+        let op = BackpropOp::new3(self, indexes, source, |t1, t2, t3| {
+            Op::Scatter(t1, t2, t3, dim)
+        });
+        Ok(from_storage(storage, self.shape(), op, false))
+    }
+
+    pub fn scatter_set<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<()> {
+        if self.same_storage(source) {
+            crate::bail!("cannot use slice_set when self and src share their storage")
+        }
+        let dim = dim.to_index(self.shape(), "scatter-set")?;
+        self.scatter_checks(indexes, source, dim)?;
+        self.storage_mut().scatter_set(
             self.layout(),
             &indexes.storage(),
             indexes.layout(),
@@ -1389,12 +1424,48 @@ impl Tensor {
             source.layout(),
             dim,
         )?;
+        Ok(())
+    }
+
+    pub fn scatter_add<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "scatter-add")?;
+        self.scatter_checks(indexes, source, dim)?;
+        let shape = self.shape();
+        let mut storage = unsafe { self.device().alloc_uninit(shape, self.dtype())? };
+        self.storage()
+            .copy_strided_src(&mut storage, 0, self.layout())?;
+        let layout = Layout::contiguous(shape);
+        storage.scatter_add(
+            &layout,
+            &indexes.storage(),
+            indexes.layout(),
+            &source.storage(),
+            source.layout(),
+            dim,
+        )?;
         let op = BackpropOp::new3(self, indexes, source, |t1, t2, t3| {
             Op::ScatterAdd(t1, t2, t3, dim)
         });
         Ok(from_storage(storage, self.shape(), op, false))
     }
 
+    pub fn scatter_add_set<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<()> {
+        if self.same_storage(source) {
+            crate::bail!("cannot use slice_set when self and src share their storage")
+        }
+        let dim = dim.to_index(self.shape(), "scatter-add-set")?;
+        self.scatter_checks(indexes, source, dim)?;
+        self.storage_mut().scatter_add(
+            self.layout(),
+            &indexes.storage(),
+            indexes.layout(),
+            &source.storage(),
+            source.layout(),
+            dim,
+        )?;
+        Ok(())
+    }
+
     /// Embeds the values of the `src` tensor into the `self` tensor on the specified dimension.
     pub fn slice_scatter<D: Dim>(&self, src: &Self, dim: D, start: usize) -> Result<Self> {
         let dim = dim.to_index(self.shape(), "slice-scatter")?;
@@ -2197,7 +2268,7 @@ impl Tensor {
     ///
     /// # Ok::<(), candle_core::Error>(())
     /// ```
-    pub fn reshape<S: crate::shape::ShapeWithOneHole>(&self, s: S) -> Result<Tensor> {
+    pub fn reshape<S: ShapeWithOneHole>(&self, s: S) -> Result<Tensor> {
         let shape = s.into_shape(self.elem_count())?;
         if shape.elem_count() != self.elem_count() {
             return Err(Error::ShapeMismatchBinaryOp {

candle-core/src/tensor_cat.rs

@@ -241,7 +241,7 @@ impl Tensor {
     /// `self` and `src` must have the same shape except on dimension `dim` where the `self` size
     /// has to be greater than or equal to `offset` plus the `src` size.
     ///
-    /// Note that this modifies `self` in place and as such is not compatibel with
+    /// Note that this modifies `self` in place and as such is not compatible with
     /// back-propagation.  
     pub fn slice_set<D: Dim>(&self, src: &Self, dim: D, offset: usize) -> Result<()> {
         let dim = dim.to_index(self.shape(), "slice-set")?;

candle-core/tests/tensor_tests.rs

@@ -25,10 +25,12 @@ fn ones(device: &Device) -> Result<()> {
         Tensor::ones((2, 3), DType::F32, device)?.to_vec2::<f32>()?,
         [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
     );
-    assert_eq!(
-        Tensor::ones((2, 3), DType::F64, device)?.to_vec2::<f64>()?,
-        [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-    );
+    if !device.is_metal() {
+        assert_eq!(
+            Tensor::ones((2, 3), DType::F64, device)?.to_vec2::<f64>()?,
+            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+        );
+    }
     assert_eq!(
         Tensor::ones((2, 3), DType::F16, device)?.to_vec2::<half::f16>()?,
         [
@@ -63,6 +65,26 @@ fn ones(device: &Device) -> Result<()> {
 }
 
 fn full(device: &Device) -> Result<()> {
+    let tensor = Tensor::zeros((3, 4), DType::U32, device)?;
+    tensor.const_set(42u32.into())?;
+    assert_eq!(
+        tensor.to_vec2::<u32>()?,
+        [[42, 42, 42, 42], [42, 42, 42, 42], [42, 42, 42, 42]]
+    );
+    tensor.i((.., 2))?.const_set(1337u32.into())?;
+    assert_eq!(
+        tensor.to_vec2::<u32>()?,
+        [[42, 42, 1337, 42], [42, 42, 1337, 42], [42, 42, 1337, 42]]
+    );
+    tensor.i((2, ..))?.const_set(1u32.into())?;
+    assert_eq!(
+        tensor.to_vec2::<u32>()?,
+        [[42, 42, 1337, 42], [42, 42, 1337, 42], [1, 1, 1, 1]]
+    );
+    Ok(())
+}
+
+fn const_set(device: &Device) -> Result<()> {
     assert_eq!(
         Tensor::full(42u32, (2, 3), device)?.to_vec2::<u32>()?,
         [[42, 42, 42], [42, 42, 42]],
@@ -826,6 +848,31 @@ fn embeddings(device: &Device) -> Result<()> {
     Ok(())
 }
 
+#[test]
+fn index_select_fail() -> Result<()> {
+    // Check that an error is properly reported on out of bounds.
+    let ids = Tensor::new(&[4u32, 2u32, 1u32], &Device::Cpu)?;
+    let t = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], &Device::Cpu)?;
+    let hs = t.index_select(&ids, 0);
+    assert!(hs.is_err());
+    Ok(())
+}
+
+// The test below triggers an unwinding panic as there is a panic within the
+// #[cfg(feature = "cuda")]
+// #[test]
+// #[should_panic]
+// fn index_select_fail_gpu() {
+//     // Check that a panic happens for out of bounds in cuda
+//     if let Ok(device) = Device::new_cuda(0) {
+//         if let Ok(ids) = Tensor::new(&[4u32, 2u32, 1u32], &device) {
+//             if let Ok(t) = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], &device) {
+//                 let _ = t.index_select(&ids, 0);
+//             }
+//         }
+//     }
+// }
+
 fn cmp(device: &Device) -> Result<()> {
     let t1 = Tensor::new(&[[0f32, 1f32], [2f32, 3f32], [4f32, 5f32]], device)?;
     let t2 = Tensor::new(&[[1f32, 0f32], [3f32, 3f32], [4f32, 7f32]], device)?;
@@ -980,7 +1027,7 @@ fn slice_scatter(device: &Device) -> Result<()> {
     Ok(())
 }
 
-fn scatter_add(device: &Device) -> Result<()> {
+fn scatter(device: &Device) -> Result<()> {
     let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
     assert_eq!(
         t.to_vec2::<f32>()?,
@@ -1004,6 +1051,17 @@ fn scatter_add(device: &Device) -> Result<()> {
         ]
     );
 
+    let hs = init.scatter(&ids, &t, 1)?;
+    assert_eq!(
+        hs.to_vec2::<f32>()?,
+        &[
+            [0.0, 1.0, 2.0, 1.0, 1.0],
+            [5.0, 1.0, 1.0, 3.0, 4.0],
+            [1.0, 8.0, 1.0, 7.0, 1.0],
+            [10.0, 1.0, 9.0, 1.0, 11.0]
+        ]
+    );
+
     let init = Tensor::ones((6, 3), DType::F32, device)?;
     let hs = init.scatter_add(&ids, &t, 0)?;
     assert_eq!(
@@ -1017,6 +1075,30 @@ fn scatter_add(device: &Device) -> Result<()> {
             [1.0, 1.0, 1.0]
         ]
     );
+    let hs = init.scatter(&ids, &t, 0)?;
+    assert_eq!(
+        hs.to_vec2::<f32>()?,
+        &[
+            [0.0, 10.0, 5.0],
+            [1.0, 1.0, 8.0],
+            [9.0, 1.0, 2.0],
+            [6.0, 7.0, 1.0],
+            [1.0, 4.0, 11.0],
+            [1.0, 1.0, 1.0]
+        ]
+    );
+    init.scatter_set(&ids, &t, 0)?;
+    assert_eq!(
+        init.to_vec2::<f32>()?,
+        &[
+            [0.0, 10.0, 5.0],
+            [1.0, 1.0, 8.0],
+            [9.0, 1.0, 2.0],
+            [6.0, 7.0, 1.0],
+            [1.0, 4.0, 11.0],
+            [1.0, 1.0, 1.0]
+        ]
+    );
     Ok(())
 }
 
@@ -1484,6 +1566,7 @@ fn zero_dim(device: &Device) -> Result<()> {
 test_device!(zeros, zeros_cpu, zeros_gpu, zeros_metal);
 test_device!(ones, ones_cpu, ones_gpu, ones_metal);
 test_device!(full, full_cpu, full_gpu, full_metal);
+test_device!(const_set, cs_cpu, cs_gpu, cs_metal);
 test_device!(arange, arange_cpu, arange_gpu, arange_metal);
 test_device!(add_mul, add_mul_cpu, add_mul_gpu, add_mul_metal);
 test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu, tensor_2d_metal);
@@ -1515,12 +1598,7 @@ test_device!(
 );
 test_device!(index_add, index_add_cpu, index_add_gpu, index_add_metal);
 test_device!(gather, gather_cpu, gather_gpu, gather_metal);
-test_device!(
-    scatter_add,
-    scatter_add_cpu,
-    scatter_add_gpu,
-    scatter_add_metal
-);
+test_device!(scatter, scatter_cpu, scatter_gpu, scatter_metal);
 test_device!(
     slice_scatter,
     slice_scatter_cpu,

candle-examples/examples/gemma/main.rs

@@ -124,6 +124,17 @@ impl TextGeneration {
             Some(token) => token,
             None => anyhow::bail!("cannot find the <eos> token"),
         };
+
+        let eot_token = match self.tokenizer.get_token("<end_of_turn>") {
+            Some(token) => token,
+            None => {
+                println!(
+                    "Warning: <end_of_turn> token not found in tokenizer, using <eos> as a backup"
+                );
+                eos_token
+            }
+        };
+
         let start_gen = std::time::Instant::now();
         for index in 0..sample_len {
             let context_size = if index > 0 { 1 } else { tokens.len() };
@@ -146,7 +157,7 @@ impl TextGeneration {
             let next_token = self.logits_processor.sample(&logits)?;
             tokens.push(next_token);
             generated_tokens += 1;
-            if next_token == eos_token {
+            if next_token == eos_token || next_token == eot_token {
                 break;
             }
             if let Some(t) = self.tokenizer.next_token(next_token)? {
@@ -350,6 +361,31 @@ fn main() -> Result<()> {
         args.repeat_last_n,
         &device,
     );
-    pipeline.run(&args.prompt, args.sample_len)?;
+
+    let prompt = match args.which {
+        Which::Base2B
+        | Which::Base7B
+        | Which::Instruct2B
+        | Which::Instruct7B
+        | Which::InstructV1_1_2B
+        | Which::InstructV1_1_7B
+        | Which::CodeBase2B
+        | Which::CodeBase7B
+        | Which::CodeInstruct2B
+        | Which::CodeInstruct7B
+        | Which::BaseV2_2B
+        | Which::InstructV2_2B
+        | Which::BaseV2_9B
+        | Which::InstructV2_9B
+        | Which::BaseV3_1B => args.prompt,
+        Which::InstructV3_1B => {
+            format!(
+                "<start_of_turn> user\n{}<end_of_turn>\n<start_of_turn> model\n",
+                args.prompt
+            )
+        }
+    };
+
+    pipeline.run(&prompt, args.sample_len)?;
     Ok(())
 }

candle-examples/examples/quantized-gemma/README.md

@@ -0,0 +1,18 @@
+# candle-quantized-gemma
+
+Candle implementation of quantized Gemma.
+
+## Running an example
+
+```bash
+$ cargo run --example quantized-gemma -- --prompt "Write a function to calculate fibonacci numbers. "
+
+> ```python
+> def fibonacci(n):
+>     """Calculates the nth Fibonacci number using recursion."""
+>     if n <= 1:
+>         return n
+>     else:
+>         return fibonacci(n-1) + fibonacci(n-2
+> ```
+```

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

@@ -0,0 +1,344 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::{Parser, ValueEnum};
+use std::io::Write;
+use tokenizers::Tokenizer;
+
+use candle::quantized::gguf_file;
+use candle::Tensor;
+use candle_transformers::generation::{LogitsProcessor, Sampling};
+
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_transformers::models::quantized_gemma3::ModelWeights;
+
+const DEFAULT_PROMPT: &str = "Write a function to calculate fibonacci num";
+
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
+enum Which {
+    #[value(name = "gemma3-4b-it")]
+    Gemma3_4bIt,
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// GGUF file to load, typically a .gguf file generated by quantization
+    #[arg(long)]
+    model: Option<String>,
+
+    /// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
+    /// and 'chat' for an interactive model where history of previous prompts and generated tokens
+    /// is preserved.
+    #[arg(long)]
+    prompt: Option<String>,
+
+    /// The length of the sample to generate (in tokens).
+    #[arg(short = 'n', long, default_value_t = 1000)]
+    sample_len: usize,
+
+    /// The tokenizer config in json format.
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    /// The temperature used to generate samples, use 0 for greedy sampling.
+    #[arg(long, default_value_t = 0.8)]
+    temperature: f64,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// Only sample among the top K samples.
+    #[arg(long)]
+    top_k: Option<usize>,
+
+    /// The seed to use when generating random samples.
+    #[arg(long, default_value_t = 299792458)]
+    seed: u64,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// Process prompt elements separately.
+    #[arg(long)]
+    split_prompt: bool,
+
+    /// Run on CPU rather than GPU even if a GPU is available.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+
+    /// The model size to use.
+    #[arg(long, default_value = "gemma3-4b-it")]
+    which: Which,
+}
+
+impl Args {
+    fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
+        let tokenizer_path = match &self.tokenizer {
+            Some(config) => std::path::PathBuf::from(config),
+            None => {
+                let api = hf_hub::api::sync::Api::new()?;
+                let repo = "google/gemma-3-4b-it";
+                println!("DEBUG: Downloading tokenizer from {}", repo);
+                let api = api.model(repo.to_string());
+                api.get("tokenizer.json")?
+            }
+        };
+        println!("DEBUG: Loading tokenizer from {:?}", tokenizer_path);
+        let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)?;
+
+        Ok(tokenizer)
+    }
+
+    fn model(&self) -> anyhow::Result<std::path::PathBuf> {
+        let model_path = match &self.model {
+            Some(config) => std::path::PathBuf::from(config),
+            None => {
+                let (repo, filename) = match self.which {
+                    Which::Gemma3_4bIt => (
+                        "google/gemma-3-4b-it-qat-q4_0-gguf",
+                        "gemma-3-4b-it-q4_0.gguf",
+                    ),
+                };
+                let api = hf_hub::api::sync::Api::new()?;
+                api.repo(hf_hub::Repo::with_revision(
+                    repo.to_string(),
+                    hf_hub::RepoType::Model,
+                    "main".to_string(),
+                ))
+                .get(filename)?
+            }
+        };
+        Ok(model_path)
+    }
+}
+
+fn format_size(size_in_bytes: usize) -> String {
+    if size_in_bytes < 1_000 {
+        format!("{}B", size_in_bytes)
+    } else if size_in_bytes < 1_000_000 {
+        format!("{:.2}KB", size_in_bytes as f64 / 1e3)
+    } else if size_in_bytes < 1_000_000_000 {
+        format!("{:.2}MB", size_in_bytes as f64 / 1e6)
+    } else {
+        format!("{:.2}GB", size_in_bytes as f64 / 1e9)
+    }
+}
+
+#[derive(Debug)]
+enum Prompt {
+    Interactive,
+    Chat,
+    One(String),
+}
+
+fn main() -> anyhow::Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
+    println!(
+        "avx: {}, neon: {}, simd128: {}, f16c: {}",
+        candle::utils::with_avx(),
+        candle::utils::with_neon(),
+        candle::utils::with_simd128(),
+        candle::utils::with_f16c()
+    );
+    println!(
+        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
+        args.temperature, args.repeat_penalty, args.repeat_last_n
+    );
+
+    let model_path = args.model()?;
+    let mut file = std::fs::File::open(&model_path)?;
+    let start = std::time::Instant::now();
+    let device = candle_examples::device(args.cpu)?;
+
+    let mut model = {
+        let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(&model_path))?;
+        let mut total_size_in_bytes = 0;
+        for (_, tensor) in model.tensor_infos.iter() {
+            let elem_count = tensor.shape.elem_count();
+            total_size_in_bytes +=
+                elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
+        }
+        println!(
+            "loaded {:?} tensors ({}) in {:.2}s",
+            model.tensor_infos.len(),
+            &format_size(total_size_in_bytes),
+            start.elapsed().as_secs_f32(),
+        );
+        ModelWeights::from_gguf(model, &mut file, &device)?
+    };
+    println!("model built");
+
+    let tokenizer = args.tokenizer()?;
+
+    let mut tos = TokenOutputStream::new(tokenizer);
+    println!(
+        "DEBUG: Tokenizer vocabulary size: {}",
+        tos.tokenizer().get_vocab(true).len()
+    );
+
+    let prompt = match args.prompt.as_deref() {
+        Some("chat") => Prompt::Chat,
+        Some("interactive") => Prompt::Interactive,
+        Some(s) => Prompt::One(s.to_string()),
+        None => Prompt::One(DEFAULT_PROMPT.to_string()),
+    };
+
+    let mut pre_prompt_tokens = vec![];
+    for _ in 0.. {
+        let prompt_str = match &prompt {
+            Prompt::One(prompt) => prompt.clone(),
+            Prompt::Interactive | Prompt::Chat => {
+                print!("> ");
+                std::io::stdout().flush()?;
+                let mut prompt = String::new();
+                std::io::stdin().read_line(&mut prompt)?;
+                if prompt.ends_with('\n') {
+                    prompt.pop();
+                    if prompt.ends_with('\r') {
+                        prompt.pop();
+                    }
+                }
+                // Format for Gemma 3 chat/instruction format
+                format!("<start_of_turn> user\n{prompt}<end_of_turn>\n<start_of_turn> model\n")
+            }
+        };
+        print!("{}", &prompt_str);
+
+        let tokens = tos
+            .tokenizer()
+            .encode(prompt_str, true)
+            .map_err(anyhow::Error::msg)?;
+        let prompt_tokens = [&pre_prompt_tokens, tokens.get_ids()].concat();
+
+        let to_sample = args.sample_len.saturating_sub(1);
+        let max_seq_len = 8192; // Gemma 3 context length
+        let prompt_tokens = if prompt_tokens.len() + to_sample > max_seq_len - 10 {
+            let to_remove = prompt_tokens.len() + to_sample + 10 - max_seq_len;
+            prompt_tokens[prompt_tokens.len().saturating_sub(to_remove)..].to_vec()
+        } else {
+            prompt_tokens
+        };
+        let mut all_tokens = vec![];
+        let mut logits_processor = {
+            let temperature = args.temperature;
+            let sampling = if temperature <= 0. {
+                Sampling::ArgMax
+            } else {
+                match (args.top_k, args.top_p) {
+                    (None, None) => Sampling::All { temperature },
+                    (Some(k), None) => Sampling::TopK { k, temperature },
+                    (None, Some(p)) => Sampling::TopP { p, temperature },
+                    (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
+                }
+            };
+            LogitsProcessor::from_sampling(args.seed, sampling)
+        };
+
+        let start_prompt_processing = std::time::Instant::now();
+        let mut next_token = if !args.split_prompt {
+            let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
+            let logits = model.forward(&input, 0)?;
+            let logits = logits.squeeze(0)?;
+            logits_processor.sample(&logits)?
+        } else {
+            let mut next_token = 0;
+            for (pos, token) in prompt_tokens.iter().enumerate() {
+                let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
+                let logits = model.forward(&input, pos)?;
+                let logits = logits.squeeze(0)?;
+                next_token = logits_processor.sample(&logits)?
+            }
+            next_token
+        };
+        let prompt_dt = start_prompt_processing.elapsed();
+        all_tokens.push(next_token);
+        if let Some(t) = tos.next_token(next_token)? {
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+
+        // For Gemma 3, use the correct end of sequence token
+        let eos_token = *tos
+            .tokenizer()
+            .get_vocab(true)
+            .get("<end_of_turn>")
+            .unwrap();
+
+        let start_post_prompt = std::time::Instant::now();
+        let mut sampled = 0;
+        for index in 0..to_sample {
+            let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
+            let logits = model.forward(&input, prompt_tokens.len() + index)?;
+            let logits = logits.squeeze(0)?;
+            let logits = if args.repeat_penalty == 1. {
+                logits
+            } else {
+                let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
+                candle_transformers::utils::apply_repeat_penalty(
+                    &logits,
+                    args.repeat_penalty,
+                    &all_tokens[start_at..],
+                )?
+            };
+            next_token = logits_processor.sample(&logits)?;
+            all_tokens.push(next_token);
+            if let Some(t) = tos.next_token(next_token)? {
+                print!("{t}");
+                std::io::stdout().flush()?;
+            }
+            sampled += 1;
+            if next_token == eos_token {
+                break;
+            };
+        }
+        if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
+            print!("{rest}");
+        }
+        std::io::stdout().flush()?;
+        let dt = start_post_prompt.elapsed();
+        println!(
+            "\n\n{:4} prompt tokens processed: {:.2} token/s",
+            prompt_tokens.len(),
+            prompt_tokens.len() as f64 / prompt_dt.as_secs_f64(),
+        );
+        println!(
+            "{sampled:4} tokens generated: {:.2} token/s",
+            sampled as f64 / dt.as_secs_f64(),
+        );
+
+        match prompt {
+            Prompt::One(_) => break,
+            Prompt::Interactive => {}
+            Prompt::Chat => {
+                pre_prompt_tokens = [prompt_tokens.as_slice(), all_tokens.as_slice()].concat()
+            }
+        }
+    }
+
+    Ok(())
+}

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.9.0-alpha.4"
+version = "0.9.0"
 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 = "0.9.0-alpha.4" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.9.0" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.9.0-alpha.4"
+version = "0.9.0"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-kernels/src/fill.cu

@@ -1,5 +1,6 @@
 #include<stdint.h>
 #include "cuda_fp16.h"
+#include "cuda_utils.cuh"
 
 template<typename T>
 __device__ void fill_with(T *buf, T value, const size_t numel) {
@@ -36,13 +37,45 @@ COPY2D_OP(uint8_t, copy2d_u8)
 COPY2D_OP(uint32_t, copy2d_u32)
 COPY2D_OP(int64_t, copy2d_i64)
 
+#define CONST_SET_OP(TYPENAME, FN_NAME) \
+extern "C" __global__ void FN_NAME( \
+    const size_t numel, \
+    const size_t num_dims, \
+    const size_t *info, \
+    const TYPENAME inp, \
+    TYPENAME *out \
+) { \
+    const size_t *dims = info; \
+    const size_t *strides = info + num_dims; \
+    if (info == nullptr || is_contiguous(num_dims, dims, strides)) { \
+        for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) { \
+            out[i] = inp; \
+        } \
+    } \
+    else { \
+        for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) { \
+            unsigned strided_i = get_strided_index(i, num_dims, dims, strides); \
+            out[strided_i] = inp; \
+        } \
+    } \
+} \
+
+CONST_SET_OP(float, const_set_f32)
+CONST_SET_OP(double, const_set_f64)
+CONST_SET_OP(uint8_t, const_set_u8)
+CONST_SET_OP(uint32_t, const_set_u32)
+CONST_SET_OP(int64_t, const_set_i64)
+
+
 #if __CUDA_ARCH__ >= 530
 extern "C" __global__ void fill_f16(__half *buf, __half value, const size_t numel) { fill_with(buf, value, numel); }
 COPY2D_OP(__half, copy2d_f16)
+CONST_SET_OP(__half, const_set_f16)
 #endif
 
 #if __CUDA_ARCH__ >= 800
 #include <cuda_bf16.h>
 extern "C" __global__ void fill_bf16(__nv_bfloat16 *buf, __nv_bfloat16 value, const size_t numel) { fill_with(buf, value, numel); }
 COPY2D_OP(__nv_bfloat16, copy2d_bf16)
+CONST_SET_OP(__nv_bfloat16, const_set_bf16)
 #endif

candle-kernels/src/indexing.cu

@@ -23,6 +23,7 @@ __device__ void index_select(
           unsigned int left_i = dst_i / (ids_dim_size * right_size);
           unsigned int id_i = dst_i / right_size % ids_dim_size;
           unsigned int right_i = dst_i % right_size;
+          assert(ids[id_i] < src_dim_size);
           unsigned int src_i = left_i * (src_dim_size * right_size) + ids[id_i] * right_size + right_i;
           unsigned strided_i = b ? src_i : get_strided_index(src_i, num_dims, dims, strides);
           out[dst_i] = inp[strided_i];
@@ -57,6 +58,7 @@ __device__ void gather(
     for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) {
         size_t post = i % right_size;
         size_t idx = ids[i];
+        assert(idx < src_dim_size);
         size_t pre = i / (right_size * ids_dim_size);
         size_t src_i = (pre * src_dim_size + idx) * right_size + post;
         out[i] = inp[src_i];
@@ -92,6 +94,7 @@ __device__ void index_add(
           const size_t post = i % right_size;
           for (unsigned int j = 0; j < ids_dim_size; ++j) {
               const size_t idx = ids[j];
+              assert(idx < dst_dim_size);
               const size_t src_i = (pre * ids_dim_size + j) * right_size + post;
               const size_t dst_i = (pre * dst_dim_size + idx) * right_size + post;
               out[dst_i] += inp[src_i];
@@ -111,6 +114,30 @@ extern "C" __global__ void FN_NAME(  \
     const size_t right_size \
 ) { index_add(ids, ids_dim_size, inp, out, left_size, src_dim_size, dst_dim_size, right_size); } \
 
+template<typename T, typename I>
+__device__ void scatter(
+    const I *ids,
+    const T *inp,
+    T *out,
+    const size_t left_size,
+    const size_t src_dim_size,
+    const size_t dst_dim_size,
+    const size_t right_size
+) {
+      const size_t numel = left_size * right_size;
+      for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) {
+          const size_t pre = i / right_size;
+          const size_t post = i % right_size;
+          for (unsigned int j = 0; j < src_dim_size; ++j) {
+              const size_t src_i = (pre * src_dim_size + j) * right_size + post;
+              const size_t idx = ids[src_i];
+              assert(idx < dst_dim_size);
+              const size_t dst_i = (pre * dst_dim_size + idx) * right_size + post;
+              out[dst_i] = inp[src_i];
+          }
+      }
+}
+
 template<typename T, typename I>
 __device__ void scatter_add(
     const I *ids,
@@ -128,12 +155,24 @@ __device__ void scatter_add(
           for (unsigned int j = 0; j < src_dim_size; ++j) {
               const size_t src_i = (pre * src_dim_size + j) * right_size + post;
               const size_t idx = ids[src_i];
+              assert(idx < dst_dim_size);
               const size_t dst_i = (pre * dst_dim_size + idx) * right_size + post;
               out[dst_i] += inp[src_i];
           }
       }
 }
 
+#define S_OP(TYPENAME, INDEX_TYPENAME, FN_NAME) \
+extern "C" __global__ void FN_NAME(  \
+    const INDEX_TYPENAME *ids, \
+    const TYPENAME *inp, \
+    TYPENAME *out, \
+    const size_t left_size, \
+    const size_t src_dim_size, \
+    const size_t dst_dim_size, \
+    const size_t right_size \
+) { scatter(ids, inp, out, left_size, src_dim_size, dst_dim_size, right_size); } \
+
 #define SA_OP(TYPENAME, INDEX_TYPENAME, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
     const INDEX_TYPENAME *ids, \
@@ -159,6 +198,9 @@ IA_OP(__nv_bfloat16, uint8_t, ia_u8_bf16)
 SA_OP(__nv_bfloat16, int64_t, sa_i64_bf16)
 SA_OP(__nv_bfloat16, uint32_t, sa_u32_bf16)
 SA_OP(__nv_bfloat16, uint8_t, sa_u8_bf16)
+S_OP(__nv_bfloat16, int64_t, s_i64_bf16)
+S_OP(__nv_bfloat16, uint32_t, s_u32_bf16)
+S_OP(__nv_bfloat16, uint8_t, s_u8_bf16)
 #endif
 
 #if __CUDA_ARCH__ >= 530
@@ -174,6 +216,9 @@ IA_OP(__half, uint8_t, ia_u8_f16)
 SA_OP(__half, int64_t, sa_i64_f16)
 SA_OP(__half, uint32_t, sa_u32_f16)
 SA_OP(__half, uint8_t, sa_u8_f16)
+S_OP(__half, int64_t, s_i64_f16)
+S_OP(__half, uint32_t, s_u32_f16)
+S_OP(__half, uint8_t, s_u8_f16)
 #endif
 
 IS_OP(float, int64_t, is_i64_f32)
@@ -247,3 +292,21 @@ SA_OP(double, uint8_t, sa_u8_f64)
 SA_OP(uint8_t, uint8_t, sa_u8_u8)
 SA_OP(uint32_t, uint8_t, sa_u8_u32)
 SA_OP(int64_t, uint8_t, sa_u8_i64)
+
+S_OP(float, int64_t, s_i64_f32)
+S_OP(double, int64_t, s_i64_f64)
+S_OP(uint8_t, int64_t, s_i64_u8)
+S_OP(int64_t, int64_t, s_i64_i64)
+S_OP(uint32_t, int64_t, s_i64_u32)
+
+S_OP(float, uint32_t, s_u32_f32)
+S_OP(double, uint32_t, s_u32_f64)
+S_OP(uint8_t, uint32_t, s_u32_u8)
+S_OP(int64_t, uint32_t, s_u32_i64)
+S_OP(uint32_t, uint32_t, s_u32_u32)
+
+S_OP(float, uint8_t, s_u8_f32)
+S_OP(double, uint8_t, s_u8_f64)
+S_OP(uint8_t, uint8_t, s_u8_u8)
+S_OP(uint32_t, uint8_t, s_u8_u32)
+S_OP(int64_t, uint8_t, s_u8_i64)

candle-metal-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.9.0-alpha.4"
+version = "0.9.0"
 edition = "2021"
 
 description = "Metal kernels for Candle"
@@ -12,6 +12,7 @@ license = "MIT OR Apache-2.0"
 
 [dependencies]
 metal = { version = "0.27.0", features = ["mps"] }
+half = { version = "2.5.0", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 once_cell = "1.18.0"
 thiserror = "1"
 tracing = "0.1.37"

candle-metal-kernels/src/fill.metal

@@ -4,20 +4,20 @@ using namespace metal;
 
 template<typename T> METAL_FUNC void fill_with(
     device T *out,
-    constant float &value,
+    constant T &value,
     constant size_t &numel,
     uint tid [[thread_position_in_grid]]
 ) {
     if (tid >= numel) {
         return;
     }
-    out[tid] = static_cast<T>(value);
+    out[tid] = value;
 }
 
 #define FILL_OP(NAME, T)                                \
 kernel void fill_##NAME(                                \
     device T *out,                                      \
-    constant float &value,                              \
+    constant T &value,                              \
     constant size_t &numel,                              \
     uint tid [[thread_position_in_grid]]                \
 ) {                                                     \

candle-metal-kernels/src/indexing.metal

@@ -104,6 +104,31 @@ kernel void NAME( \
     gather<TYPENAME, INDEX_TYPENAME>(dst_size, left_size, src_dim_size, right_size, ids_size, input, input_ids, output, tid); \
 }
 
+template<typename TYPENAME, typename INDEX_TYPENAME>
+METAL_FUNC void scatter(
+    constant size_t &dst_size,
+    constant size_t &left_size,
+    constant size_t &src_dim_size,
+    constant size_t &right_size,
+    constant size_t &dst_dim_size,
+    const device TYPENAME *input,
+    const device INDEX_TYPENAME *input_ids,
+    device TYPENAME *output,
+    uint tid [[ thread_position_in_grid ]]
+) {
+    if (tid >= dst_size) {
+        return;
+    }
+    const size_t right_rank_i = tid % right_size;
+    const size_t left_rank_i = tid / right_size;
+    for (unsigned int j = 0; j < src_dim_size; ++j) {
+        const size_t src_i = (left_rank_i * src_dim_size + j) * right_size + right_rank_i;
+        const INDEX_TYPENAME idx = input_ids[src_i];
+        const size_t dst_i = (left_rank_i * dst_dim_size + idx) * right_size + right_rank_i;
+        output[dst_i] = input[src_i];
+    }
+}
+
 template<typename TYPENAME, typename INDEX_TYPENAME>
 METAL_FUNC void scatter_add(
     constant size_t &dst_size,
@@ -129,6 +154,21 @@ METAL_FUNC void scatter_add(
     }
 }
 
+# define SCATTER_OP(NAME, INDEX_TYPENAME, TYPENAME) \
+kernel void NAME( \
+    constant size_t &dst_size, \
+    constant size_t &left_size, \
+    constant size_t &src_dim_size, \
+    constant size_t &right_size, \
+    constant size_t &dst_dim_size, \
+    const device TYPENAME *input, \
+    const device INDEX_TYPENAME *input_ids, \
+    device TYPENAME *output, \
+    uint tid [[ thread_position_in_grid ]] \
+) { \
+    scatter<TYPENAME, INDEX_TYPENAME>(dst_size, left_size, src_dim_size, right_size, dst_dim_size, input, input_ids, output, tid); \
+}
+
 # define SCATTER_ADD_OP(NAME, INDEX_TYPENAME, TYPENAME) \
 kernel void NAME( \
     constant size_t &dst_size, \
@@ -235,6 +275,19 @@ SCATTER_ADD_OP(sa_u8_bf16, uint8_t, bfloat)
 SCATTER_ADD_OP(sa_i64_bf16, int64_t, bfloat)
 #endif
 
+SCATTER_OP(s_u32_f32, uint32_t, float)
+SCATTER_OP(s_u8_f32, uint8_t, float)
+SCATTER_OP(s_i64_f32, int64_t, float)
+SCATTER_OP(s_u32_u32, uint32_t, uint32_t)
+SCATTER_OP(s_u32_f16, uint32_t, half)
+SCATTER_OP(s_u8_f16, uint8_t, half)
+SCATTER_OP(s_i64_f16, int64_t, half)
+#if defined(__HAVE_BFLOAT__)
+SCATTER_OP(s_u32_bf16, uint32_t, bfloat)
+SCATTER_OP(s_u8_bf16, uint8_t, bfloat)
+SCATTER_OP(s_i64_bf16, int64_t, bfloat)
+#endif
+
 // i64
 INDEX_ADD_OP(ia_i64_f16, int64_t, half)
 INDEX_ADD_OP(ia_i64_f32, int64_t, float)

candle-metal-kernels/src/lib.rs

@@ -161,7 +161,7 @@ macro_rules! ops{
 pub mod unary {
     ops!(
         cos, sin, exp, sqr, sqrt, neg, log, gelu, abs, ceil, floor, relu, round, erf, gelu_erf,
-        tanh, recip, silu, sign, sigmoid
+        tanh, recip, silu, sign, sigmoid, const_set
     );
 }
 pub mod binary {
@@ -419,6 +419,82 @@ pub fn call_copy2d(
     Ok(())
 }
 
+#[allow(clippy::too_many_arguments)]
+pub fn call_const_set_contiguous_tiled(
+    device: &Device,
+    ep: impl EncoderProvider,
+    kernels: &Kernels,
+    kernel_name: unary::contiguous_tiled::Kernel,
+    length: usize,
+    input: impl EncoderParam,
+    output: BufferOffset,
+) -> Result<(), MetalKernelError> {
+    let pipeline = kernels.load_pipeline(device, Source::Unary, kernel_name.0)?;
+    let encoder = ep.encoder();
+    let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
+    let tile_size = 2;
+    let tiles = length.div_ceil(tile_size);
+
+    encoder.set_compute_pipeline_state(&pipeline);
+
+    set_params!(encoder, (length, input, &output));
+
+    let (thread_group_count, thread_group_size) = linear_split(&pipeline, tiles);
+    encoder.use_resource(output.buffer, metal::MTLResourceUsage::Write);
+    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    Ok(())
+}
+
+#[allow(clippy::too_many_arguments)]
+pub fn call_const_set_contiguous(
+    device: &Device,
+    ep: impl EncoderProvider,
+    kernels: &Kernels,
+    kernel_name: unary::contiguous::Kernel,
+    length: usize,
+    input: impl EncoderParam,
+    output: BufferOffset,
+) -> Result<(), MetalKernelError> {
+    let pipeline = kernels.load_pipeline(device, Source::Unary, kernel_name.0)?;
+    let encoder = ep.encoder();
+    let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
+
+    encoder.set_compute_pipeline_state(&pipeline);
+
+    set_params!(encoder, (length, input, &output));
+
+    let (thread_group_count, thread_group_size) = linear_split(&pipeline, length);
+    encoder.use_resource(output.buffer, metal::MTLResourceUsage::Write);
+    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    Ok(())
+}
+
+#[allow(clippy::too_many_arguments)]
+pub fn call_const_set_strided(
+    device: &Device,
+    ep: impl EncoderProvider,
+    kernels: &Kernels,
+    name: unary::strided::Kernel,
+    shape: &[usize],
+    input: impl EncoderParam,
+    strides: &[usize],
+    output: BufferOffset,
+) -> Result<(), MetalKernelError> {
+    let pipeline = kernels.load_pipeline(device, Source::Unary, name.0)?;
+
+    let length: usize = shape.iter().product();
+    let num_dims: usize = shape.len();
+    let encoder = ep.encoder();
+    let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
+    let (thread_group_count, thread_group_size) = linear_split(&pipeline, length);
+
+    encoder.set_compute_pipeline_state(&pipeline);
+    set_params!(encoder, (length, num_dims, shape, strides, input, &output));
+    encoder.use_resource(output.buffer, metal::MTLResourceUsage::Write);
+    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    Ok(())
+}
+
 #[allow(clippy::too_many_arguments)]
 pub fn call_unary_contiguous_tiled(
     device: &Device,
@@ -1371,7 +1447,7 @@ pub fn call_gather(
 }
 
 #[allow(clippy::too_many_arguments)]
-pub fn call_scatter_add(
+pub fn call_scatter(
     device: &Device,
     ep: impl EncoderProvider,
     kernels: &Kernels,
@@ -1381,7 +1457,7 @@ pub fn call_scatter_add(
     dim: usize,
     input: BufferOffset,
     ids: BufferOffset,
-    output: &Buffer,
+    output: BufferOffset,
 ) -> Result<(), MetalKernelError> {
     let left_size: usize = src_shape[..dim].iter().product();
     let right_size: usize = src_shape[dim + 1..].iter().product();
@@ -1406,7 +1482,7 @@ pub fn call_scatter_add(
             dst_dim_size,
             &input,
             &ids,
-            output
+            &output
         )
     );
 
@@ -1414,7 +1490,7 @@ pub fn call_scatter_add(
 
     encoder.use_resource(input.buffer, metal::MTLResourceUsage::Read);
     encoder.use_resource(ids.buffer, metal::MTLResourceUsage::Read);
-    encoder.use_resource(output, metal::MTLResourceUsage::Write);
+    encoder.use_resource(output.buffer, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
     Ok(())
 }
@@ -2570,7 +2646,7 @@ pub fn call_const_fill(
     name: &'static str,
     length: usize,
     output: &Buffer,
-    v: f32,
+    v: impl EncoderParam,
 ) -> Result<(), MetalKernelError> {
     let pipeline = kernels.load_pipeline(device, Source::Fill, name)?;
     let encoder = ep.encoder();

candle-metal-kernels/src/tests.rs

@@ -1574,7 +1574,7 @@ fn run_scatter_add<T: Clone, I: Clone + std::fmt::Debug>(
     let input_buffer = new_buffer(&device, input);
     let ids_buffer = new_buffer(&device, ids);
     let output = device.new_buffer(std::mem::size_of_val(input) as u64, options);
-    call_scatter_add(
+    call_scatter(
         &device,
         command_buffer,
         &kernels,
@@ -2343,7 +2343,7 @@ fn conv_transpose1d_u32() {
 
 #[test]
 fn const_fill() {
-    fn constant_fill<T: Clone>(name: &'static str, len: usize, value: f32) -> Vec<T> {
+    fn constant_fill<T: Clone + EncoderParam>(name: &'static str, len: usize, value: T) -> Vec<T> {
         let dev = device();
         let kernels = Kernels::new();
         let command_queue = dev.new_command_queue();
@@ -2357,11 +2357,15 @@ fn const_fill() {
         command_buffer.wait_until_completed();
         read_to_vec::<T>(&buffer, len)
     }
-    fn test<T: Clone + PartialEq + std::fmt::Debug, F: FnOnce(f32) -> T>(name: &'static str, f: F) {
+    fn test<T: Clone + Copy + EncoderParam + PartialEq + std::fmt::Debug, F: FnOnce(f32) -> T>(
+        name: &'static str,
+        f: F,
+    ) {
         let len = rand::thread_rng().gen_range(2..16) * rand::thread_rng().gen_range(4..16);
         let value = rand::thread_rng().gen_range(1. ..19.);
+        let value = f(value);
         let v = constant_fill::<T>(name, len, value);
-        assert_eq!(v, vec![f(value); len])
+        assert_eq!(v, vec![value; len])
     }
     test::<u8, _>("fill_u8", |v| v as u8);
     test::<u32, _>("fill_u32", |v| v as u32);

candle-metal-kernels/src/unary.metal

@@ -73,6 +73,44 @@ template <typename T> METAL_FUNC T sigmoid(T in) {
 
 #define TILE_SIZE 2
 
+#define CONST_SET(TYPENAME, FN_NAME) \
+kernel void FN_NAME( \
+    constant size_t &dim, \
+    constant TYPENAME &input,  \
+    device TYPENAME *output, \
+    uint tid [[ thread_position_in_grid ]] \
+) { \
+    if (tid >= dim) { \
+        return; \
+    } \
+    output[tid] = input; \
+} \
+kernel void FN_NAME##_##strided( \
+    constant size_t &dim, \
+    constant size_t &num_dims, \
+    constant size_t *dims, \
+    constant size_t *strides, \
+    constant TYPENAME &input,  \
+    device TYPENAME *output, \
+    uint tid [[ thread_position_in_grid ]] \
+) { \
+    if (tid >= dim) { \
+        return; \
+    } \
+    output[get_strided_index(tid, num_dims, dims, strides)] = input; \
+} \
+kernel void FN_NAME##_##tiled( \
+    constant size_t &dim, \
+    constant TYPENAME &input,  \
+    device TYPENAME *output, \
+    uint tid [[ thread_position_in_grid ]] \
+) { \
+    for (uint i = 0; i < TILE_SIZE; i++) { \
+        const uint idx = tid * TILE_SIZE + i; \
+        output[idx] = input; \
+    } \
+}
+
 #define UNARY(FN, TYPENAME, FN_NAME, FN_NAME_STRIDED) \
 kernel void FN_NAME( \
     constant size_t &dim, \
@@ -139,6 +177,11 @@ COPY2D(copy2d_f16, half)
 COPY2D(copy2d_u8, uint8_t)
 COPY2D(copy2d_u32, uint32_t)
 
+CONST_SET(float, const_set_f32)
+CONST_SET(half, const_set_f16)
+CONST_SET(uint8_t, const_set_u8)
+CONST_SET(uint32_t, const_set_u32)
+
 UNARY_OP(cos)
 UNARY_OP(sin)
 UNARY_OP(sqr)
@@ -171,6 +214,7 @@ UNARY(precise::tanh, half, tanh_f16, tanh_f16_strided);
 #if __METAL_VERSION__ >= 220
 UNARY(id, int64_t, copy_i64, copy_i64_strided)
 COPY2D(copy2d_i64, int64_t)
+CONST_SET(int64_t, const_set_i64)
 #endif
 
 #if defined(__HAVE_BFLOAT__)
@@ -199,4 +243,5 @@ UNARY(id, bfloat, copy_bf16, copy_bf16_strided)
 UNARY(precise::tanh, bfloat, tanh_bf16, tanh_bf16_strided);
 
 COPY2D(copy2d_bf16, bfloat)
+CONST_SET(bfloat, const_set_bf16)
 #endif

candle-metal-kernels/src/utils.rs

@@ -88,9 +88,13 @@ primitive!(bool);
 primitive!(usize);
 primitive!(i32);
 primitive!(i64);
+primitive!(u8);
 primitive!(u32);
 primitive!(u64);
 primitive!(f32);
+primitive!(f64);
+primitive!(half::bf16);
+primitive!(half::f16);
 
 pub struct BufferOffset<'a> {
     pub buffer: &'a Buffer,

candle-nn/src/activation.rs

@@ -71,6 +71,8 @@ impl candle::Module for PReLU {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let weight = if self.is_scalar {
             self.weight.reshape(())?
+        } else if xs.shape() == self.weight.shape() {
+            self.weight.clone()
         } else if xs.rank() >= 2 {
             let num_channels = xs.dim(1)?;
             let num_weights = self.weight.elem_count();
@@ -78,7 +80,7 @@ impl candle::Module for PReLU {
                 candle::bail!("error in prelu: unexpected number of channels for the input, got {num_channels}, weight dim is {num_weights}")
             }
             let mut s = vec![1; xs.rank()];
-            s[1] = self.weight.elem_count();
+            s[1] = num_weights;
             self.weight.reshape(s)?
         } else {
             self.weight.clone()

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.9.0-alpha.4"
+version = "0.9.0"
 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 = "0.9.0-alpha.4" }
-candle-nn = { path = "../candle-nn", version = "0.9.0-alpha.4" }
+candle = { path = "../candle-core", package = "candle-core", version = "0.9.0" }
+candle-nn = { path = "../candle-nn", version = "0.9.0" }
 prost = "0.12.1"
 
 [build-dependencies]

candle-onnx/src/eval.rs

@@ -1,7 +1,9 @@
 use crate::onnx::attribute_proto::AttributeType;
 use crate::onnx::tensor_proto::DataType;
 use crate::onnx::{self, GraphProto};
+use candle::Module;
 use candle::{bail, DType, Device, Result, Tensor};
+use candle_nn::activation::PReLU;
 use std::collections::{HashMap, HashSet};
 
 pub type Value = Tensor;
@@ -991,6 +993,14 @@ fn simple_eval_(
                 let output = input.relu()?;
                 values.insert(node.output[0].clone(), output);
             }
+            "PRelu" => {
+                // https://onnx.ai/onnx/operators/onnx__PRelu.html
+                let input = get(&node.input[0])?;
+                let slope = get(&node.input[1])?;
+
+                let output = PReLU::new(slope.clone(), false).forward(input)?;
+                values.insert(node.output[0].clone(), output);
+            }
             "Ceil" => {
                 let input = get(&node.input[0])?;
                 let output = input.ceil()?;

candle-onnx/tests/ops.rs

@@ -1846,6 +1846,64 @@ fn test_relu_operation() -> Result<()> {
     Ok(())
 }
 
+// "PRelu"
+#[test]
+fn test_prelu_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "PRelu".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.to_string(), INPUT_Y.to_string()],
+            output: vec![OUTPUT_Z.to_string()],
+            name: "".to_string(),
+            doc_string: "".to_string(),
+        }],
+        name: "".to_string(),
+        initializer: vec![],
+        input: vec![
+            ValueInfoProto {
+                name: INPUT_X.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            },
+            ValueInfoProto {
+                name: INPUT_Y.to_string(),
+                doc_string: "".to_string(),
+                r#type: None,
+            },
+        ],
+        output: vec![ValueInfoProto {
+            name: OUTPUT_Z.to_string(),
+            doc_string: "".to_string(),
+            r#type: None,
+        }],
+        value_info: vec![],
+        doc_string: "".to_string(),
+        sparse_initializer: vec![],
+        quantization_annotation: vec![],
+    }));
+    let x: Tensor = Tensor::from_vec(
+        vec![-1.0f32, 1.0f32, -2.0f32, 3.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+
+    let y: Tensor = Tensor::from_vec(vec![1.0f32, 1.1f32, 1.2f32, 1.3f32], &[2, 2], &Device::Cpu)?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+    inputs.insert(INPUT_Y.to_string(), y);
+
+    let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
+    assert_eq!(eval.len(), 1);
+
+    let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
+    let results = z.to_vec2::<f32>()?;
+    assert_eq!(results, vec![vec![-1.0, 1.0], vec![-2.4, 3.0]]);
+
+    Ok(())
+}
 // "Constant"
 // #[test]
 

candle-transformers/src/models/gemma3.rs

@@ -21,6 +21,7 @@ pub struct Config {
     pub num_key_value_heads: usize,
     pub rms_norm_eps: f64,
     pub rope_theta: f64,
+    pub rope_local_base_freq: f64,
     pub vocab_size: usize,
     pub final_logit_softcapping: Option<f64>,
     pub attn_logit_softcapping: Option<f64>,
@@ -67,12 +68,22 @@ struct RotaryEmbedding {
 }
 
 impl RotaryEmbedding {
-    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
+    fn new(
+        dtype: DType,
+        cfg: &Config,
+        dev: &Device,
+        sliding_window: Option<usize>,
+    ) -> Result<Self> {
         let dim = cfg.head_dim;
         let max_seq_len = cfg.max_position_embeddings;
+        let rope_freq = if sliding_window.is_some() {
+            cfg.rope_local_base_freq
+        } else {
+            cfg.rope_theta
+        };
         let inv_freq: Vec<_> = (0..dim)
             .step_by(2)
-            .map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
+            .map(|i| 1f32 / rope_freq.powf(i as f64 / dim as f64) as f32)
             .collect();
         let inv_freq_len = inv_freq.len();
         let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
@@ -162,8 +173,8 @@ impl Attention {
     fn new(
         rotary_emb: Arc<RotaryEmbedding>,
         use_flash_attn: bool,
-        is_sliding: bool,
         cfg: &Config,
+        sliding_window: Option<usize>,
         vb: VarBuilder,
     ) -> Result<Self> {
         let hidden_sz = cfg.hidden_size;
@@ -178,13 +189,13 @@ impl Attention {
         let o_proj = linear(num_heads * head_dim, hidden_sz, bias, vb.pp("o_proj"))?;
         let q_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("q_norm"))?;
         let k_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("k_norm"))?;
-        let kv_cache = if is_sliding {
-            KvCache::Rotating(candle_nn::kv_cache::RotatingKvCache::new(
-                2,
-                cfg.sliding_window,
-            ))
+        let kv_cache = if let Some(sliding_window) = sliding_window {
+            KvCache::Rotating(candle_nn::kv_cache::RotatingKvCache::new(2, sliding_window))
         } else {
-            KvCache::Normal(candle_nn::kv_cache::KvCache::new(2, cfg.sliding_window))
+            KvCache::Normal(candle_nn::kv_cache::KvCache::new(
+                2,
+                cfg.max_position_embeddings,
+            ))
         };
         Ok(Self {
             q_proj,
@@ -302,21 +313,27 @@ struct DecoderLayer {
     pre_feedforward_layernorm: RmsNorm,
     post_feedforward_layernorm: RmsNorm,
     post_attention_layernorm: RmsNorm,
+    sliding_window: Option<usize>,
 }
 
 impl DecoderLayer {
     fn new(
-        rotary_emb: Arc<RotaryEmbedding>,
         use_flash_attn: bool,
-        is_sliding: bool,
         cfg: &Config,
         vb: VarBuilder,
+        sliding_window: Option<usize>,
     ) -> Result<Self> {
+        let rotary_emb = Arc::new(RotaryEmbedding::new(
+            vb.dtype(),
+            cfg,
+            vb.device(),
+            sliding_window,
+        )?);
         let self_attn = Attention::new(
             rotary_emb,
             use_flash_attn,
-            is_sliding,
             cfg,
+            sliding_window,
             vb.pp("self_attn"),
         )?;
         let mlp = MLP::new(cfg, vb.pp("mlp"))?;
@@ -344,6 +361,7 @@ impl DecoderLayer {
             pre_feedforward_layernorm,
             post_feedforward_layernorm,
             post_attention_layernorm,
+            sliding_window,
         })
     }
 
@@ -370,6 +388,42 @@ impl DecoderLayer {
     }
 }
 
+fn prepare_decoder_attention_mask(
+    b_size: usize,
+    tgt_len: usize,
+    seqlen_offset: usize,
+    sliding_window: Option<usize>,
+    dtype: DType,
+    device: &Device,
+) -> Result<Tensor> {
+    let mask: Vec<_> = if let Some(sliding_window) = sliding_window {
+        (0..tgt_len)
+            .flat_map(|i| {
+                (0..tgt_len).map(move |j| {
+                    if i < j || j + sliding_window < i {
+                        f32::NEG_INFINITY
+                    } else {
+                        0.
+                    }
+                })
+            })
+            .collect()
+    } else {
+        (0..tgt_len)
+            .flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0f32 }))
+            .collect()
+    };
+    let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), device)?;
+    let mask = if seqlen_offset > 0 {
+        let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, device)?;
+        Tensor::cat(&[&mask0, &mask], D::Minus1)?
+    } else {
+        mask
+    };
+    mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
+        .to_dtype(dtype)
+}
+
 #[derive(Debug, Clone)]
 pub struct Model {
     embed_tokens: candle_nn::Embedding,
@@ -388,17 +442,15 @@ impl Model {
         let vb_m = vb.pp("model");
         let embed_tokens =
             candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
-        let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
         let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
         let vb_l = vb_m.pp("layers");
         for layer_idx in 0..cfg.num_hidden_layers {
-            let is_sliding = (layer_idx + 1) % cfg.sliding_window_pattern > 0;
+            let sliding_window = (layer_idx + 1) % cfg.sliding_window_pattern > 0;
             let layer = DecoderLayer::new(
-                rotary_emb.clone(),
                 use_flash_attn,
-                is_sliding,
                 cfg,
                 vb_l.pp(layer_idx),
+                sliding_window.then_some(cfg.sliding_window),
             )?;
             layers.push(layer)
         }
@@ -417,51 +469,52 @@ impl Model {
         })
     }
 
-    fn prepare_decoder_attention_mask(
+    fn create_attention_masks(
         &self,
-        b_size: usize,
-        tgt_len: usize,
+        batch_size: usize,
+        seq_len: usize,
         seqlen_offset: usize,
-    ) -> Result<Tensor> {
-        let mask: Vec<_> = match Some(self.sliding_window) {
-            None => (0..tgt_len)
-                .flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0. }))
-                .collect(),
-            Some(sliding_window) => (0..tgt_len)
-                .flat_map(|i| {
-                    (0..tgt_len).map(move |j| {
-                        if i < j || j + sliding_window < i {
-                            f32::NEG_INFINITY
-                        } else {
-                            0.
-                        }
-                    })
-                })
-                .collect(),
-        };
-        let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
-        let mask = if seqlen_offset > 0 {
-            let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
-            Tensor::cat(&[&mask0, &mask], D::Minus1)?
-        } else {
-            mask
-        };
-        mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
-            .to_dtype(self.dtype)
+    ) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        if seq_len <= 1 {
+            return Ok((None, None));
+        }
+
+        let mask = prepare_decoder_attention_mask(
+            batch_size,
+            seq_len,
+            seqlen_offset,
+            None,
+            self.dtype,
+            &self.device,
+        )?;
+
+        let sliding_mask = prepare_decoder_attention_mask(
+            batch_size,
+            seq_len,
+            seqlen_offset,
+            Some(self.sliding_window),
+            self.dtype,
+            &self.device,
+        )?;
+
+        Ok((Some(mask), Some(sliding_mask)))
     }
 
     pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
         let (b_size, seq_len) = input_ids.dims2()?;
-        let attention_mask = if seq_len <= 1 {
-            None
-        } else {
-            let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
-            Some(mask)
-        };
         let xs = self.embed_tokens.forward(input_ids)?;
         let mut xs = (xs * (self.hidden_size as f64).sqrt())?;
+
+        let (attention_mask, sliding_attention_mask) =
+            self.create_attention_masks(b_size, seq_len, seqlen_offset)?;
+
         for layer in self.layers.iter_mut() {
-            xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
+            let mask = if layer.sliding_window.is_some() {
+                &sliding_attention_mask
+            } else {
+                &attention_mask
+            };
+            xs = layer.forward(&xs, mask.as_ref(), seqlen_offset)?
         }
         let logits = xs
             .narrow(1, seq_len - 1, 1)?

candle-transformers/src/models/mod.rs

@@ -79,6 +79,7 @@ pub mod phi3;
 pub mod pixtral;
 pub mod quantized_blip;
 pub mod quantized_blip_text;
+pub mod quantized_gemma3;
 pub mod quantized_llama;
 pub mod quantized_llama2_c;
 pub mod quantized_metavoice;

candle-transformers/src/models/quantized_gemma3.rs

@@ -0,0 +1,466 @@
+//! Gemma 3 model implementation with quantization support.
+//!
+//! Gemma 3 is a family of multimodal language models developed by Google.
+//! This implementation provides quantization for reduced memory usage and faster inference.
+//!
+//! Key characteristics:
+//! - Group-Query Attention (GQA) with specialized key-value heads
+//! - RMSNorm for layer normalization
+//! - Specialized attention patterns with separate normalization for Q/K/V
+//! - Feed-forward network with SwiGLU activation
+//! - Support for 2/3/4/8-bit quantization
+//!
+//! References:
+//! - [Gemma 3 Models](https://blog.google/technology/developers/gemma-3/)
+//!
+
+use crate::quantized_nn::RmsNorm;
+use candle::quantized::gguf_file;
+use candle::quantized::QTensor;
+use candle::D;
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module};
+
+pub const MAX_SEQ_LEN: usize = 131072; // Gemma 3 supports 128K context window
+pub const DEFAULT_SLIDING_WINDOW_TYPE: usize = 6;
+pub const DEFAULT_ROPE_FREQUENCY: f32 = 1_000_000.;
+pub const DEFAULT_ROPE_FREQUENCY_SLIDING: f32 = 10_000.;
+pub const DEFAULT_ROPE_FREQUENCY_SCALE_FACTOR: f32 = 1.;
+
+#[derive(Debug, Clone)]
+struct QMatMul {
+    inner: candle::quantized::QMatMul,
+    span: tracing::Span,
+}
+
+impl QMatMul {
+    fn from_qtensor(qtensor: QTensor) -> Result<Self> {
+        let inner = candle::quantized::QMatMul::from_qtensor(qtensor)?;
+        let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
+        Ok(Self { inner, span })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    feed_forward_gate: QMatMul, // ffn_gate in GGUF
+    feed_forward_up: QMatMul,   // ffn_up in GGUF
+    feed_forward_down: QMatMul, // ffn_down in GGUF
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let gate = self.feed_forward_gate.forward(xs)?;
+        let up = self.feed_forward_up.forward(xs)?;
+        let silu = candle_nn::ops::silu(&gate)?;
+        let gated = (silu * up)?;
+        self.feed_forward_down.forward(&gated)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(head_dim: usize, rope_frequency: f32, device: &Device) -> Result<Self> {
+        let theta: Vec<_> = (0..head_dim)
+            .step_by(2)
+            .map(|i| 1f32 / rope_frequency.powf(i as f32 / head_dim as f32))
+            .collect();
+        let theta = Tensor::new(theta.as_slice(), device)?;
+        let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
+            .to_dtype(DType::F32)?
+            .reshape((MAX_SEQ_LEN, 1))?
+            .matmul(&theta.reshape((1, theta.elem_count()))?)?;
+        let cos = idx_theta.cos()?;
+        let sin = idx_theta.sin()?;
+        Ok(Self { sin, cos })
+    }
+
+    fn apply_rotary_emb_qkv(
+        &self,
+        q: &Tensor,
+        k: &Tensor,
+        index_pos: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, index_pos, seq_len)?;
+        let sin = self.sin.narrow(0, index_pos, seq_len)?;
+        let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
+        let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
+        Ok((q_embed, k_embed))
+    }
+}
+
+#[derive(Debug, Clone)]
+struct LayerWeights {
+    // Attention components
+    attention_wq: QMatMul,
+    attention_wk: QMatMul,
+    attention_wv: QMatMul,
+    attention_wo: QMatMul,
+
+    // Specialized normalization for Q and K
+    attention_q_norm: RmsNorm,
+    attention_k_norm: RmsNorm,
+
+    // Layer normalization
+    attention_norm: RmsNorm,      // Applied before attention
+    post_attention_norm: RmsNorm, // Applied after attention
+    ffn_norm: RmsNorm,            // Applied before feedforward
+    post_ffn_norm: RmsNorm,       // Applied after feedforward
+
+    // Feed-forward network
+    mlp: Mlp,
+
+    // Attention parameters
+    n_head: usize,    // Number of query heads
+    n_kv_head: usize, // Number of key-value heads
+    head_dim: usize,  // Dimension of each head
+    q_dim: usize,     // Total dimension for queries
+
+    sliding_window_size: Option<usize>,
+
+    rotary_embedding: RotaryEmbedding,
+    neg_inf: Tensor,
+
+    // Cache
+    kv_cache: Option<(Tensor, Tensor)>,
+
+    // Tracing
+    span_attn: tracing::Span,
+    span_mlp: tracing::Span,
+}
+
+impl LayerWeights {
+    fn mask(
+        &self,
+        b_sz: usize,
+        seq_len: usize,
+        index_pos: usize,
+        dtype: DType,
+        device: &Device,
+    ) -> Result<Tensor> {
+        let mask: Vec<_> = if let Some(sliding_window_size) = self.sliding_window_size {
+            (0..seq_len)
+                .flat_map(|i| {
+                    (0..seq_len).map(move |j| {
+                        if i < j || j + sliding_window_size < i {
+                            0u32
+                        } else {
+                            1u32
+                        }
+                    })
+                })
+                .collect()
+        } else {
+            (0..seq_len)
+                .flat_map(|i| (0..seq_len).map(move |j| if i < j { 0u32 } else { 1u32 }))
+                .collect()
+        };
+        let mask = Tensor::from_slice(&mask, (seq_len, seq_len), device)?;
+        let mask = if index_pos > 0 {
+            let mask0 = Tensor::zeros((seq_len, index_pos), DType::F32, device)?;
+            Tensor::cat(&[&mask0, &mask], D::Minus1)?
+        } else {
+            mask
+        };
+        mask.expand((b_sz, 1, seq_len, seq_len + index_pos))?
+            .to_dtype(dtype)
+    }
+
+    fn forward_attn(
+        &mut self,
+        x: &Tensor,
+        mask: Option<&Tensor>,
+        index_pos: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span_attn.enter();
+        let (b_sz, seq_len, _) = x.dims3()?;
+
+        let q = self.attention_wq.forward(x)?;
+        let k = self.attention_wk.forward(x)?;
+        let v = self.attention_wv.forward(x)?;
+
+        let q = q
+            .reshape((b_sz, seq_len, self.n_head, self.head_dim))?
+            .transpose(1, 2)?;
+        let k = k
+            .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))?
+            .transpose(1, 2)?;
+        let v = v
+            .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))?
+            .transpose(1, 2)?;
+
+        let q = self.attention_q_norm.forward(&q.contiguous()?)?;
+        let k = self.attention_k_norm.forward(&k.contiguous()?)?;
+
+        let (q, k) = self
+            .rotary_embedding
+            .apply_rotary_emb_qkv(&q, &k, index_pos)?;
+
+        let (k, v) = match &self.kv_cache {
+            None => (k, v),
+            Some((k_cache, v_cache)) => {
+                if index_pos == 0 {
+                    (k, v)
+                } else {
+                    let k = Tensor::cat(&[k_cache, &k], 2)?; // concat on seq dim
+                    let v = Tensor::cat(&[v_cache, &v], 2)?;
+                    (k, v)
+                }
+            }
+        };
+        self.kv_cache = Some((k.clone(), v.clone())); // update cache
+
+        // Repeat KV for GQA
+        let k = crate::utils::repeat_kv(k, self.n_head / self.n_kv_head)?;
+        let v = crate::utils::repeat_kv(v, self.n_head / self.n_kv_head)?;
+
+        // Scaled Dot-Product Attention
+        let scale = 1.0 / (self.head_dim as f64).sqrt();
+        let mut attn_weights = (q.matmul(&k.transpose(2, 3)?)? * scale)?;
+
+        if let Some(mask) = mask {
+            let mask = mask.broadcast_as(attn_weights.shape())?;
+            let neg_inf = self.neg_inf.broadcast_as(attn_weights.dims())?;
+            attn_weights = mask.eq(0u32)?.where_cond(&neg_inf, &attn_weights)?;
+        }
+
+        let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+        let attn_output = attn_weights.matmul(&v)?;
+
+        let attn_output = attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, seq_len, self.q_dim))?;
+
+        self.attention_wo.forward(&attn_output)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct ModelWeights {
+    tok_embeddings: Embedding,
+    embedding_length: usize,
+    layers: Vec<LayerWeights>,
+    norm: RmsNorm,
+    output: QMatMul,
+    span: tracing::Span,
+    span_output: tracing::Span,
+}
+
+impl ModelWeights {
+    pub fn from_gguf<R: std::io::Seek + std::io::Read>(
+        ct: gguf_file::Content,
+        reader: &mut R,
+        device: &Device,
+    ) -> Result<Self> {
+        let md_get = |s: &str| match ct.metadata.get(s) {
+            None => candle::bail!("cannot find {s} in metadata"),
+            Some(v) => Ok(v),
+        };
+
+        let head_count = md_get("gemma3.attention.head_count")?.to_u32()? as usize;
+        let head_count_kv = md_get("gemma3.attention.head_count_kv")?.to_u32()? as usize;
+        let block_count = md_get("gemma3.block_count")?.to_u32()? as usize;
+        let embedding_length = md_get("gemma3.embedding_length")?.to_u32()? as usize;
+        let key_length = md_get("gemma3.attention.key_length")?.to_u32()? as usize;
+        let _value_length = md_get("gemma3.attention.value_length")?.to_u32()? as usize;
+        let rms_norm_eps = md_get("gemma3.attention.layer_norm_rms_epsilon")?.to_f32()? as f64;
+        let sliding_window_size = md_get("gemma3.attention.sliding_window")?.to_u32()? as usize;
+
+        let sliding_window_type = md_get("gemma3.attention.sliding_window_type")
+            .and_then(|m| Ok(m.to_u32()? as usize))
+            .unwrap_or(DEFAULT_SLIDING_WINDOW_TYPE);
+
+        let rope_freq_base = md_get("gemma3.rope.freq_base")
+            .and_then(|m| m.to_f32())
+            .unwrap_or(DEFAULT_ROPE_FREQUENCY);
+
+        let rope_freq_base_sliding = md_get("gemma3.rope.local_freq_base")
+            .and_then(|m| m.to_f32())
+            .unwrap_or(DEFAULT_ROPE_FREQUENCY_SLIDING);
+
+        // Unused in Llama.cpp so we aren't using it here.
+        let _rope_freq_scaling_factor = md_get("gemma3.rope.scaling.factor")
+            .and_then(|m| m.to_f32())
+            .unwrap_or(DEFAULT_ROPE_FREQUENCY_SCALE_FACTOR);
+
+        // Compute the dimensions for queries, keys, and values
+        // These are the total dimensions when projected across all heads
+        let q_dim = head_count * key_length;
+
+        let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?;
+
+        // Load token embeddings and output projection
+        let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?;
+        let tok_embeddings = tok_embeddings.dequantize(device)?;
+        let norm = RmsNorm::from_qtensor(
+            ct.tensor(reader, "output_norm.weight", device)?,
+            rms_norm_eps,
+        )?;
+        let output = match ct.tensor(reader, "output.weight", device) {
+            Ok(tensor) => tensor,
+            Err(_) => ct.tensor(reader, "token_embd.weight", device)?, // Use tied weights if output.weight doesn't exist
+        };
+
+        let mut layers = Vec::with_capacity(block_count);
+        for layer_idx in 0..block_count {
+            let prefix = format!("blk.{layer_idx}");
+
+            let attention_wq = ct.tensor(reader, &format!("{prefix}.attn_q.weight"), device)?;
+            let attention_wk = ct.tensor(reader, &format!("{prefix}.attn_k.weight"), device)?;
+            let attention_wv = ct.tensor(reader, &format!("{prefix}.attn_v.weight"), device)?;
+            let attention_wo =
+                ct.tensor(reader, &format!("{prefix}.attn_output.weight"), device)?;
+
+            let attention_q_norm = RmsNorm::from_qtensor(
+                ct.tensor(reader, &format!("{prefix}.attn_q_norm.weight"), device)?,
+                rms_norm_eps,
+            )?;
+
+            let attention_k_norm = RmsNorm::from_qtensor(
+                ct.tensor(reader, &format!("{prefix}.attn_k_norm.weight"), device)?,
+                rms_norm_eps,
+            )?;
+
+            let attention_norm = RmsNorm::from_qtensor(
+                ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?,
+                rms_norm_eps,
+            )?;
+
+            let post_attention_norm = RmsNorm::from_qtensor(
+                ct.tensor(
+                    reader,
+                    &format!("{prefix}.post_attention_norm.weight"),
+                    device,
+                )?,
+                rms_norm_eps,
+            )?;
+
+            let ffn_norm = RmsNorm::from_qtensor(
+                ct.tensor(reader, &format!("{prefix}.ffn_norm.weight"), device)?,
+                rms_norm_eps,
+            )?;
+
+            let post_ffn_norm = RmsNorm::from_qtensor(
+                ct.tensor(reader, &format!("{prefix}.post_ffw_norm.weight"), device)?,
+                rms_norm_eps,
+            )?;
+
+            let feed_forward_gate =
+                ct.tensor(reader, &format!("{prefix}.ffn_gate.weight"), device)?;
+            let feed_forward_up = ct.tensor(reader, &format!("{prefix}.ffn_up.weight"), device)?;
+            let feed_forward_down =
+                ct.tensor(reader, &format!("{prefix}.ffn_down.weight"), device)?;
+
+            let mlp = Mlp {
+                feed_forward_gate: QMatMul::from_qtensor(feed_forward_gate)?,
+                feed_forward_up: QMatMul::from_qtensor(feed_forward_up)?,
+                feed_forward_down: QMatMul::from_qtensor(feed_forward_down)?,
+            };
+
+            // Sliding window pattern hardcoded to 6 because it's not explicitly defined
+            let is_sliding = (layer_idx + 1) % sliding_window_type > 0;
+            let sliding_window_size = is_sliding.then_some(sliding_window_size);
+            let layer_rope_frequency = if is_sliding {
+                rope_freq_base_sliding
+            } else {
+                rope_freq_base
+            };
+
+            let rotary_embedding = RotaryEmbedding::new(key_length, layer_rope_frequency, device)?;
+
+            // Tracing spans
+            let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
+            let span_mlp = tracing::span!(tracing::Level::TRACE, "attn-mlp");
+
+            layers.push(LayerWeights {
+                attention_wq: QMatMul::from_qtensor(attention_wq)?,
+                attention_wk: QMatMul::from_qtensor(attention_wk)?,
+                attention_wv: QMatMul::from_qtensor(attention_wv)?,
+                attention_wo: QMatMul::from_qtensor(attention_wo)?,
+                attention_q_norm,
+                attention_k_norm,
+                attention_norm,
+                post_attention_norm,
+                ffn_norm,
+                post_ffn_norm,
+                mlp,
+                n_head: head_count,
+                n_kv_head: head_count_kv,
+                head_dim: key_length,
+                q_dim,
+                sliding_window_size,
+                rotary_embedding,
+                neg_inf: neg_inf.clone(),
+                kv_cache: None,
+                span_attn,
+                span_mlp,
+            })
+        }
+
+        let span = tracing::span!(tracing::Level::TRACE, "model");
+        let span_output = tracing::span!(tracing::Level::TRACE, "output");
+
+        Ok(Self {
+            tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
+            embedding_length,
+            layers,
+            norm,
+            output: QMatMul::from_qtensor(output)?,
+            span,
+            span_output,
+        })
+    }
+
+    pub fn forward(&mut self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (b_sz, seq_len) = x.dims2()?;
+        let _enter = self.span.enter();
+
+        let mut layer_in = self.tok_embeddings.forward(x)?;
+        layer_in = (layer_in * (self.embedding_length as f64).sqrt())?;
+
+        for layer in self.layers.iter_mut() {
+            let attention_mask = if seq_len == 1 {
+                None
+            } else {
+                Some(layer.mask(b_sz, seq_len, index_pos, x.dtype(), x.device())?)
+            };
+
+            // Attention block
+            let residual = &layer_in;
+            let x = layer.attention_norm.forward(&layer_in)?;
+            let x = layer.forward_attn(&x, attention_mask.as_ref(), index_pos)?;
+            let x = layer.post_attention_norm.forward(&x)?;
+            let x = (x + residual)?;
+
+            // Feed-forward block
+            let _enter = layer.span_mlp.enter();
+            let residual = &x;
+            let x = layer.ffn_norm.forward(&x)?;
+            let x = layer.mlp.forward(&x)?;
+            let x = layer.post_ffn_norm.forward(&x)?;
+            let x = (x + residual)?;
+            drop(_enter);
+
+            layer_in = x;
+        }
+
+        let _enter = self.span_output.enter();
+
+        let x = layer_in.i((.., seq_len - 1, ..))?;
+        let x = self.norm.forward(&x)?;
+        let output = self.output.forward(&x)?;
+
+        Ok(output)
+    }
+}