Better version ?

Fix NaN errors for Gelu in Metal

Cargo.toml

@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.3.0"
+version = "0.3.1"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -51,6 +51,7 @@ rayon = "1.7.0"
 rusttype = { version = "0.9", default-features = false }
 safetensors = "0.3.1"
 serde = { version = "1.0.171", features = ["derive"] }
+serde_plain = "1.0.2"
 serde_json = "1.0.99"
 thiserror = "1"
 tokenizers = { version = "0.13.4", default-features = false }
@@ -60,8 +61,7 @@ tracing-subscriber = "0.3.7"
 wav = "1.0.0"
 yoke = { version = "0.7.2", features = ["derive"] }
 zip = { version = "0.6.6", default-features = false }
-# metal = { git = "https://github.com/ivarflakstad/metal-rs.git", features = ["mps"] }
-metal = { path = "../metal-rs", features = ["mps"] }
+metal = { version = "0.27.1", features = ["mps"], package="candle-metal" }
 
 [profile.release-with-debug]
 inherits = "release"

README.md

@@ -69,6 +69,8 @@ We also provide a some command line based examples using state of the art models
   performance larger than all publicly available 13b models as of 2023-09-28.
 - [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
 - [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
+- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
+  (English/Chinese) general LLMs with 6b and 34b parameters.
 - [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
   the LLaMA model using the same quantization techniques as
   [llama.cpp](https://github.com/ggerganov/llama.cpp).
@@ -137,16 +139,16 @@ And then head over to
 <!--- ANCHOR: useful_libraries --->
 
 ## Useful External Resources
-- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): a
+- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): A
   very detailed tutorial showing how to convert a PyTorch model to Candle.
-- [`optimisers`](https://github.com/KGrewal1/optimisers): a collection of optimisers
+- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): Efficient and ergonomic LoRA implemenation for Candle. `candle-lora` has      
+  out-of-the-box LoRA support for many models from Candle, which can be found [here](https://github.com/EricLBuehler/candle-lora/tree/master/candle-lora-transformers/examples).
+- [`optimisers`](https://github.com/KGrewal1/optimisers): A collection of optimisers
   including SGD with momentum, AdaGrad, AdaDelta, AdaMax, NAdam, RAdam, and RMSprop.
-- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): a LoRA implementation
-  that conforms to the official `peft` implementation.
 - [`candle-vllm`](https://github.com/EricLBuehler/candle-vllm): Efficient platform for inference and
   serving local LLMs including an OpenAI compatible API server.
-- [`candle-ext`](https://github.com/mokeyish/candle-ext): an extension library to Candle that provides PyTorch functions not currently available in Candle.
-- [`kalosm`](https://github.com/floneum/floneum/tree/master/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
+- [`candle-ext`](https://github.com/mokeyish/candle-ext): An extension library to Candle that provides PyTorch functions not currently available in Candle.
+- [`kalosm`](https://github.com/floneum/floneum/tree/master/interfaces/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
 - [`candle-sampling`](https://github.com/EricLBuehler/candle-sampling): Sampling techniques for Candle.
 
 If you have an addition to this list, please submit a pull request.
@@ -174,8 +176,14 @@ If you have an addition to this list, please submit a pull request.
         - StableLM-3B-4E1T.
         - Replit-code-v1.5-3B.
         - Bert.
+        - Yi-6B and Yi-34B.
+    - Quantized LLMs.
+        - Llama 7b, 13b, 70b, as well as the chat and code variants.
+        - Mistral 7b, and 7b instruct.
+        - Zephyr 7b a and b (Mistral based).
+        - OpenChat 3.5 (Mistral based).
     - Text to text.
-        - T5 and its variants: FlanT5, MADLAD400 (translation), CoEdit (Grammar correction).
+        - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
         - Marian MT (Machine Translation).
     - Whisper (multi-lingual support).
     - Text to image.

candle-book/Cargo.toml

@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.3.0" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../candle-transformers", version = "0.3.0" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.3.1" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../candle-transformers", version = "0.3.1" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.1", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

candle-core/Cargo.toml

@@ -12,8 +12,8 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.3.0", optional = true }
-candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.0", optional = true }
+candle-kernels = { path = "../candle-kernels", version = "0.3.1", optional = true }
+candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.1", optional = true }
 metal = { workspace = true, optional = true}
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }

candle-core/examples/basics.rs

@@ -8,11 +8,10 @@ use anyhow::Result;
 use candle_core::{Device, Tensor};
 
 fn main() -> Result<()> {
-    let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
-    let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
-    let start = std::time::Instant::now();
-    let res = inp.conv2d(&w, 0, 1, 1, 1)?;
-    println!("{:?}", start.elapsed());
-    println!("{res:?}");
+    let a = Tensor::new(&[[0.0f32, 1.0, 2.0], [3.0, 4.0, 5.0]], &Device::Cpu)?;
+    let b = Tensor::new(&[[88.0f32, 99.0]], &Device::Cpu)?;
+    let new_a = a.slice_scatter(&b, 1, 2)?;
+    assert_eq!(a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
+    assert_eq!(new_a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
     Ok(())
 }

candle-core/src/indexer.rs

@@ -104,37 +104,31 @@ impl From<&Tensor> for TensorIndexer {
     }
 }
 
-macro_rules! impl_from_range {
-    ($range_type:ty) => {
-        impl From<$range_type> for TensorIndexer {
-            fn from(range: $range_type) -> Self {
-                use std::ops::Bound::*;
+trait RB: RangeBounds<usize> {}
+impl RB for Range<usize> {}
+impl RB for RangeFrom<usize> {}
+impl RB for RangeFull {}
+impl RB for RangeInclusive<usize> {}
+impl RB for RangeTo<usize> {}
+impl RB for RangeToInclusive<usize> {}
 
-                let start = match range.start_bound() {
-                    Included(idx) => Included(*idx),
-                    Excluded(idx) => Excluded(*idx),
-                    Unbounded => Unbounded,
-                };
-
-                let end = match range.end_bound() {
-                    Included(idx) => Included(*idx),
-                    Excluded(idx) => Excluded(*idx),
-                    Unbounded => Unbounded,
-                };
-
-                TensorIndexer::Narrow(start, end)
-            }
-        }
-    };
+impl<T: RB> From<T> for TensorIndexer {
+    fn from(range: T) -> Self {
+        use std::ops::Bound::*;
+        let start = match range.start_bound() {
+            Included(idx) => Included(*idx),
+            Excluded(idx) => Excluded(*idx),
+            Unbounded => Unbounded,
+        };
+        let end = match range.end_bound() {
+            Included(idx) => Included(*idx),
+            Excluded(idx) => Excluded(*idx),
+            Unbounded => Unbounded,
+        };
+        TensorIndexer::Narrow(start, end)
+    }
 }
 
-impl_from_range!(Range<usize>);
-impl_from_range!(RangeFrom<usize>);
-impl_from_range!(RangeFull);
-impl_from_range!(RangeInclusive<usize>);
-impl_from_range!(RangeTo<usize>);
-impl_from_range!(RangeToInclusive<usize>);
-
 /// Trait used to implement multiple signatures for ease of use of the slicing
 /// of a tensor
 pub trait IndexOp<T> {

candle-core/src/lib.rs

@@ -123,12 +123,6 @@ pub trait Module {
     fn forward(&self, xs: &Tensor) -> Result<Tensor>;
 }
 
-impl Module for quantized::QMatMul {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        self.forward(xs)
-    }
-}
-
 impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         self(xs)

candle-core/src/op.rs

@@ -593,7 +593,8 @@ unary_op!(Recip, "recip", v, v.recip());
 unary_op!(Sqr, "sqr", v, v * v, vs_sqr, vd_sqr);
 unary_op!(Sqrt, "sqrt", v, v.sqrt(), vs_sqrt, vd_sqrt);
 
-/// `gelu` operation
+/// Tanh based approximation of the `gelu` operation
+/// GeluErf is the more precise one.
 /// <https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions>
 impl UnaryOpT for Gelu {
     const NAME: &'static str = "gelu";

candle-core/src/quantized/mod.rs

@@ -307,8 +307,8 @@ impl crate::CustomOp1 for QTensor {
     }
 }
 
-impl QMatMul {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl crate::Module for QMatMul {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         match self {
             Self::QTensor(t) => xs.apply_op1_no_bwd(t.as_ref()),
             Self::Tensor(w) => {

candle-core/src/tensor.rs

@@ -157,8 +157,6 @@ pub(crate) fn from_storage<S: Into<Shape>>(
 ) -> Tensor {
     let dtype = storage.dtype();
     let device = storage.device();
-    let shape = shape.into();
-    // println!("{:?} {storage:?}", shape);
     let tensor_ = Tensor_ {
         id: TensorId::new(),
         storage: Arc::new(RwLock::new(storage)),
@@ -168,11 +166,7 @@ pub(crate) fn from_storage<S: Into<Shape>>(
         dtype,
         device,
     };
-    let result = Tensor(Arc::new(tensor_));
-    // todo!(" from_storage");
-    // let result = result.to_device(&Device::Cpu).unwrap();
-    // todo!(" {result}");
-    result
+    Tensor(Arc::new(tensor_))
 }
 
 impl Tensor {
@@ -862,6 +856,20 @@ impl Tensor {
         self.sum_impl(mean_dims, false)? * scale
     }
 
+    /// Returns the unbiased variance over the selected dimension.
+    pub fn var_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "var")?;
+        let mean = self.mean_keepdim(dim)?;
+        let squares = self.broadcast_sub(&mean)?.sqr()?;
+        squares.sum_impl(dim, true)? / (self.dim(dim)? - 1) as f64
+    }
+
+    /// Returns the unbiased variance over the selected dimension.
+    pub fn var<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "var")?;
+        self.var_keepdim(dim)?.squeeze(dim)
+    }
+
     /// Gathers the maximum value across the selected dimension. The resulting shape has the same
     /// number of dimensions as the original tensor and the select dimension has a single element.
     pub fn max_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
@@ -1855,7 +1863,10 @@ impl Tensor {
                     Storage::Metal(metal.storage_from_cpu_storage(storage)?)
                 }
                 (Storage::Cuda(storage), Device::Cpu) => Storage::Cpu(storage.to_cpu_storage()?),
-                (Storage::Metal(storage), Device::Cpu) => Storage::Cpu(storage.to_cpu_storage()?),
+                (Storage::Metal(storage), Device::Cpu) => {
+                    // println!("{storage:?} - {:?}", storage.to_cpu_storage()?);
+                    Storage::Cpu(storage.to_cpu_storage()?)
+                }
                 (Storage::Cuda(storage), Device::Cuda(cuda)) => {
                     // TODO: Avoid passing through the cpu storage here, especially if the gpu ids
                     // are the same.
@@ -2446,6 +2457,110 @@ impl Tensor {
             Ok(naxis as usize)
         }
     }
+
+    /// Returns a lower triangular matrix of ones of size n by n.
+    pub fn tril2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.le(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns an upper triangular matrix of ones of size n by n.
+    pub fn triu2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.ge(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns a matrix with a diagonal of ones of size n by n.
+    pub fn eye(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.eq(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns the cumulative sum of elements of the input tensor summed over the specified
+    /// dimension.
+    ///
+    /// This operation is most efficient when dim is the last dimension of the tensor.
+    pub fn cumsum<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "cumsum")?;
+        let rank = self.rank();
+        if rank == 0 {
+            return Ok(self.clone());
+        }
+        let n_axis = self.dim(dim)?;
+        let triu = Tensor::triu2(n_axis, self.dtype(), self.device())?;
+        if rank == 1 {
+            self.unsqueeze(0)?.matmul(&triu)?.squeeze(0)
+        } else {
+            let last = rank - 1;
+            let t = self.transpose(dim, last)?;
+            let t = t.broadcast_matmul(&triu)?;
+            t.transpose(dim, last)
+        }
+    }
+
+    /// Returns a copy of `self` where the values within `ranges` have been replaced with the
+    /// content of `src`.
+    pub fn slice_assign<D: std::ops::RangeBounds<usize>>(
+        &self,
+        ranges: &[D],
+        src: &Tensor,
+    ) -> Result<Self> {
+        let src_dims = src.dims();
+        let self_dims = self.dims();
+        if self_dims.len() != src_dims.len() {
+            crate::bail!(
+                "slice-assign requires input with the same rank {} <> {}",
+                self_dims.len(),
+                src_dims.len()
+            )
+        }
+        if self_dims.len() != ranges.len() {
+            crate::bail!(
+                "slice-assign requires input with the same rank as there are ranges {} <> {}",
+                self_dims.len(),
+                ranges.len()
+            )
+        }
+        let mut src = src.clone();
+        let mut mask = Self::ones(src.shape(), DType::U8, src.device())?;
+        for (i, range) in ranges.iter().enumerate() {
+            let start_included = match range.start_bound() {
+                std::ops::Bound::Unbounded => 0,
+                std::ops::Bound::Included(v) => *v,
+                std::ops::Bound::Excluded(v) => *v + 1,
+            };
+            let end_excluded = match range.end_bound() {
+                std::ops::Bound::Unbounded => self_dims[i],
+                std::ops::Bound::Included(v) => *v + 1,
+                std::ops::Bound::Excluded(v) => *v,
+            };
+            if end_excluded <= start_included {
+                crate::bail!(
+                    "slice-assign: empty range for dim {i}, {start_included} {end_excluded}"
+                )
+            }
+            if self_dims[i] < end_excluded {
+                crate::bail!(
+                    "slice-assign: upper bound is out of range for dim {i}, {end_excluded} {}",
+                    self_dims[i]
+                )
+            }
+            if end_excluded - start_included != src_dims[i] {
+                crate::bail!(
+                    "slice-assign: the range for dim {i} ({start_included}..{end_excluded}) does not match the size of src {}", src_dims[i]
+                )
+            }
+            src = src.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?;
+            mask = mask.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?
+        }
+        mask.where_cond(/* on_true= */ &src, /* on_false= */ self)
+    }
 }
 
 macro_rules! bin_trait {

candle-core/src/test_utils.rs

@@ -4,7 +4,7 @@ use crate::{Result, Tensor};
 macro_rules! test_device {
     // TODO: Switch to generating the two last arguments automatically once concat_idents is
     // stable. https://github.com/rust-lang/rust/issues/29599
-    ($fn_name: ident, $test_cpu: ident, $test_cuda: ident) => {
+    ($fn_name: ident, $test_cpu: ident, $test_cuda: ident, $test_metal: ident) => {
         #[test]
         fn $test_cpu() -> Result<()> {
             $fn_name(&Device::Cpu)
@@ -15,6 +15,12 @@ macro_rules! test_device {
         fn $test_cuda() -> Result<()> {
             $fn_name(&Device::new_cuda(0)?)
         }
+
+        #[cfg(feature = "metal")]
+        #[test]
+        fn $test_metal() -> Result<()> {
+            $fn_name(&Device::new_metal(0)?)
+        }
     };
 }
 

candle-core/tests/conv_tests.rs

@@ -563,14 +563,35 @@ fn conv2d_grad(dev: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(conv1d, conv1d_cpu, conv1d_gpu);
-test_device!(conv1d_small, conv1d_small_cpu, conv1d_small_gpu);
-test_device!(conv2d, conv2d_cpu, conv2d_gpu);
+test_device!(conv1d, conv1d_cpu, conv1d_gpu, conv1d_metal);
+test_device!(
+    conv1d_small,
+    conv1d_small_cpu,
+    conv1d_small_gpu,
+    conv1d_small_metal
+);
+test_device!(conv2d, conv2d_cpu, conv2d_gpu, conv2d_metal);
 test_device!(
     conv2d_non_square,
     conv2d_non_square_cpu,
-    conv2d_non_square_gpu
+    conv2d_non_square_gpu,
+    conv2d_non_square_metal
+);
+test_device!(
+    conv2d_small,
+    conv2d_small_cpu,
+    conv2d_small_gpu,
+    conv2d_small_metal
+);
+test_device!(
+    conv2d_smaller,
+    conv2d_smaller_cpu,
+    conv2d_smaller_gpu,
+    conv2d_smaller_metal
+);
+test_device!(
+    conv2d_grad,
+    conv2d_grad_cpu,
+    conv2d_grad_gpu,
+    conv2_grad_metal
 );
-test_device!(conv2d_small, conv2d_small_cpu, conv2d_small_gpu);
-test_device!(conv2d_smaller, conv2d_smaller_cpu, conv2d_smaller_gpu);
-test_device!(conv2d_grad, conv2d_grad_cpu, conv2d_grad_gpu);

candle-core/tests/grad_tests.rs

@@ -315,9 +315,29 @@ fn binary_grad(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(simple_grad, simple_grad_cpu, simple_grad_gpu);
-test_device!(sum_grad, sum_grad_cpu, sum_grad_gpu);
-test_device!(matmul_grad, matmul_grad_cpu, matmul_grad_gpu);
-test_device!(grad_descent, grad_descent_cpu, grad_descent_gpu);
-test_device!(unary_grad, unary_grad_cpu, unary_grad_gpu);
-test_device!(binary_grad, binary_grad_cpu, binary_grad_gpu);
+test_device!(
+    simple_grad,
+    simple_grad_cpu,
+    simple_grad_gpu,
+    simple_grad_metal
+);
+test_device!(sum_grad, sum_grad_cpu, sum_grad_gpu, sum_grad_metal);
+test_device!(
+    matmul_grad,
+    matmul_grad_cpu,
+    matmul_grad_gpu,
+    matmul_grad_metal
+);
+test_device!(
+    grad_descent,
+    grad_descent_cpu,
+    grad_descent_gpu,
+    grad_descent_metal
+);
+test_device!(unary_grad, unary_grad_cpu, unary_grad_gpu, unary_grad_metal);
+test_device!(
+    binary_grad,
+    binary_grad_cpu,
+    binary_grad_gpu,
+    binary_grad_metal
+);

candle-core/tests/indexing_tests.rs

@@ -91,3 +91,32 @@ fn index_3d() -> Result<()> {
     assert_eq!(tensor.i((1, .., 3))?.to_vec1::<u32>()?, &[15, 19, 23]);
     Ok(())
 }
+
+#[test]
+fn slice_assign() -> Result<()> {
+    let dev = Device::Cpu;
+
+    let tensor = Tensor::arange(0u32, 4 * 5, &dev)?.reshape((4, 5))?;
+    let src = Tensor::arange(0u32, 2 * 3, &dev)?.reshape((3, 2))?;
+    let out = tensor.slice_assign(&[1..4, 3..5], &src)?;
+    assert_eq!(
+        out.to_vec2::<u32>()?,
+        &[
+            [0, 1, 2, 3, 4],
+            [5, 6, 7, 0, 1],
+            [10, 11, 12, 2, 3],
+            [15, 16, 17, 4, 5]
+        ]
+    );
+    let out = tensor.slice_assign(&[0..3, 0..2], &src)?;
+    assert_eq!(
+        out.to_vec2::<u32>()?,
+        &[
+            [0, 1, 2, 3, 4],
+            [2, 3, 7, 8, 9],
+            [4, 5, 12, 13, 14],
+            [15, 16, 17, 18, 19]
+        ]
+    );
+    Ok(())
+}

candle-core/tests/layout_tests.rs

@@ -49,7 +49,7 @@ fn contiguous(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(contiguous, contiguous_cpu, contiguous_gpu);
+test_device!(contiguous, contiguous_cpu, contiguous_gpu, contiguous_metal);
 
 #[test]
 fn strided_blocks() -> Result<()> {

candle-core/tests/pool_tests.rs

@@ -98,15 +98,17 @@ fn upsample_nearest2d(dev: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(avg_pool2d, avg_pool2d_cpu, avg_pool2d_gpu);
+test_device!(avg_pool2d, avg_pool2d_cpu, avg_pool2d_gpu, avg_pool2d_metal);
 test_device!(
     avg_pool2d_pytorch,
     avg_pool2d_pytorch_cpu,
-    avg_pool2d_pytorch_gpu
+    avg_pool2d_pytorch_gpu,
+    avg_pool2d_pytorch_metal
 );
-test_device!(max_pool2d, max_pool2d_cpu, max_pool2d_gpu);
+test_device!(max_pool2d, max_pool2d_cpu, max_pool2d_gpu, max_pool2d_metal);
 test_device!(
     upsample_nearest2d,
     upsample_nearest2d_cpu,
-    upsample_nearest2d_gpu
+    upsample_nearest2d_gpu,
+    upsample_nearest2d_metal
 );

candle-core/tests/quantized_tests.rs

@@ -1,7 +1,7 @@
 use candle_core::{
     quantized::{self, GgmlDType},
     test_utils::to_vec2_round,
-    Device, Result, Tensor,
+    Device, Module, Result, Tensor,
 };
 use quantized::{k_quants, GgmlType};
 use rand::prelude::*;

candle-core/tests/tensor_tests.rs

@@ -180,6 +180,22 @@ fn transpose(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn var(device: &Device) -> Result<()> {
+    // Values taken from https://pytorch.org/docs/stable/generated/torch.var.html
+    let data = &[
+        [0.2035f32, 1.2959, 1.8101, -0.4644],
+        [1.5027, -0.3270, 0.5905, 0.6538],
+        [-1.5745, 1.3330, -0.5596, -0.6548],
+        [0.1264, -0.5080, 1.6420, 0.1992],
+    ];
+    let tensor = Tensor::new(data, device)?;
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.var_keepdim(1)?, 4)?,
+        &[[1.0631], [0.559], [1.4893], [0.8258]]
+    );
+    Ok(())
+}
+
 fn sum(device: &Device) -> Result<()> {
     let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
     let tensor = Tensor::new(data, device)?;
@@ -1054,34 +1070,60 @@ fn randn(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(zeros, zeros_cpu, zeros_gpu);
-test_device!(ones, ones_cpu, ones_gpu);
-test_device!(arange, arange_cpu, arange_gpu);
-test_device!(add_mul, add_mul_cpu, add_mul_gpu);
-test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
-test_device!(narrow, narrow_cpu, narrow_gpu);
-test_device!(broadcast, broadcast_cpu, broadcast_gpu);
-test_device!(cat, cat_cpu, cat_gpu);
-test_device!(sum, sum_cpu, sum_gpu);
-test_device!(min, min_cpu, min_gpu);
-test_device!(max, max_cpu, max_gpu);
-test_device!(argmax, argmax_cpu, argmax_gpu);
-test_device!(argmin, argmin_cpu, argmin_gpu);
-test_device!(transpose, transpose_cpu, transpose_gpu);
-test_device!(unary_op, unary_op_cpu, unary_op_gpu);
-test_device!(binary_op, binary_op_cpu, binary_op_gpu);
-test_device!(embeddings, embeddings_cpu, embeddings_gpu);
-test_device!(cmp, cmp_cpu, cmp_gpu);
-test_device!(matmul, matmul_cpu, matmul_gpu);
-test_device!(broadcast_matmul, broadcast_matmul_cpu, broadcast_matmul_gpu);
-test_device!(broadcasting, broadcasting_cpu, broadcasting_gpu);
-test_device!(index_select, index_select_cpu, index_select_gpu);
-test_device!(index_add, index_add_cpu, index_add_gpu);
-test_device!(gather, gather_cpu, gather_gpu);
-test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
-test_device!(slice_scatter, slice_scatter_cpu, slice_scatter_gpu);
-test_device!(randn, randn_cpu, randn_gpu);
-test_device!(clamp, clamp_cpu, clamp_gpu);
+test_device!(zeros, zeros_cpu, zeros_gpu, zeros_metal);
+test_device!(ones, ones_cpu, ones_gpu, ones_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);
+test_device!(narrow, narrow_cpu, narrow_gpu, narrow_metal);
+test_device!(broadcast, broadcast_cpu, broadcast_gpu, broadcast_metal);
+test_device!(cat, cat_cpu, cat_gpu, cat_metal);
+test_device!(sum, sum_cpu, sum_gpu, sum_metal);
+test_device!(min, min_cpu, min_gpu, min_metal);
+test_device!(max, max_cpu, max_gpu, max_metal);
+test_device!(argmax, argmax_cpu, argmax_gpu, argmax_metal);
+test_device!(argmin, argmin_cpu, argmin_gpu, argmin_metal);
+test_device!(transpose, transpose_cpu, transpose_gpu, transpose_metal);
+test_device!(unary_op, unary_op_cpu, unary_op_gpu, unary_op_metal);
+test_device!(binary_op, binary_op_cpu, binary_op_gpu, binary_op_metal);
+test_device!(embeddings, embeddings_cpu, embeddings_gpu, embeddings_metal);
+test_device!(cmp, cmp_cpu, cmp_gpu, cmp_metal);
+test_device!(matmul, matmul_cpu, matmul_gpu, matmul_metal);
+test_device!(
+    broadcast_matmul,
+    broadcast_matmul_cpu,
+    broadcast_matmul_gpu,
+    broadcast_matmul_metal
+);
+test_device!(
+    broadcasting,
+    broadcasting_cpu,
+    broadcasting_gpu,
+    broadcasting_metal
+);
+test_device!(
+    index_select,
+    index_select_cpu,
+    index_select_gpu,
+    index_select_metal
+);
+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!(
+    slice_scatter,
+    slice_scatter_cpu,
+    slice_scatter_gpu,
+    slice_scatter_metal
+);
+test_device!(randn, randn_cpu, randn_gpu, randn_metal);
+test_device!(clamp, clamp_cpu, clamp_gpu, clamp_metal);
+test_device!(var, var_cpu, var_gpu, var_metal);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381
@@ -1117,3 +1159,65 @@ fn i64_abs() -> Result<()> {
     assert_eq!(t.to_vec1::<i64>()?, [42, 1337]);
     Ok(())
 }
+
+#[test]
+fn tril_triu_eye() -> Result<()> {
+    let t = Tensor::tril2(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 0.0, 0.0, 0.0],
+            [1.0, 1.0, 0.0, 0.0],
+            [1.0, 1.0, 1.0, 0.0],
+            [1.0, 1.0, 1.0, 1.0]
+        ],
+    );
+    let t = Tensor::triu2(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 1.0, 1.0, 1.0],
+            [0.0, 1.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0]
+        ]
+    );
+    let t = Tensor::eye(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 0.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0]
+        ]
+    );
+    Ok(())
+}
+
+#[test]
+fn cumsum() -> Result<()> {
+    let t = &[3f32, 1., 4., 1., 5.];
+    let t = Tensor::new(t, &Device::Cpu)?;
+    assert_eq!(t.cumsum(0)?.to_vec1::<f32>()?, [3., 4., 8., 9., 14.]);
+    let t = t.unsqueeze(1)?;
+    assert_eq!(
+        t.cumsum(0)?.to_vec2::<f32>()?,
+        [[3.0], [4.0], [8.0], [9.0], [14.0]]
+    );
+    assert_eq!(
+        t.cumsum(1)?.to_vec2::<f32>()?,
+        [[3.0], [1.0], [4.0], [1.0], [5.0]]
+    );
+    let t = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
+    let t = Tensor::new(t, &Device::Cpu)?;
+    assert_eq!(
+        t.cumsum(1)?.to_vec2::<f32>()?,
+        [[3.0, 4.0, 8.0, 9.0, 14.0], [2.0, 3.0, 10.0, 18.0, 20.0]],
+    );
+    assert_eq!(
+        t.cumsum(0)?.to_vec2::<f32>()?,
+        [[3.0, 1.0, 4.0, 1.0, 5.0], [5.0, 2.0, 11.0, 9.0, 7.0]]
+    );
+    Ok(())
+}

candle-datasets/Cargo.toml

@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
 hf-hub = { workspace = true}
 intel-mkl-src = { workspace = true, optional = true }
 memmap2 = { workspace = true }

candle-examples/Cargo.toml

@@ -11,12 +11,12 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.3.0" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../candle-transformers", version = "0.3.0" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
-candle-onnx = { path = "../candle-onnx", version = "0.3.0", optional = true }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.3.1" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../candle-transformers", version = "0.3.1" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.1", optional = true }
+candle-onnx = { path = "../candle-onnx", version = "0.3.1", optional = true }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
@@ -57,6 +57,7 @@ flash-attn = ["cuda", "candle-transformers/flash-attn", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl", "candle-transformers/mkl"]
 nccl = ["cuda", "cudarc/nccl", "dep:half"]
 onnx = ["candle-onnx"]
+metal = ["candle/metal", "candle-nn/metal"]
 
 [[example]]
 name = "llama_multiprocess"

candle-examples/examples/distilbert/README.md

@@ -0,0 +1,22 @@
+# candle-distilbert
+
+DistilBert is a distiled version of the Bert model.
+
+## Sentence embeddings
+
+DistilBert is used to compute the sentence embeddings for a prompt. The model weights
+are downloaded from the hub on the first run.
+
+```bash
+cargo run --example distilbert --release -- --prompt "Here is a test sentence"
+
+> [[[ 0.5109,  0.1280, -0.2635, ...,  0.3462, -1.0434,  0.1441],
+>   [ 0.1735,  0.0818, -0.5549, ...,  0.3472, -0.8264, -0.0244],
+>   [ 0.0702, -0.1311, -0.4914, ...,  0.3483, -0.6194,  0.1829],
+>   ...
+>   [ 0.2993, -0.0106, -0.4640, ...,  0.2844, -0.6732,  0.0042],
+>   [ 0.1066, -0.0081, -0.4299, ...,  0.3435, -0.7729,  0.0190],
+>   [ 0.8903,  0.2055, -0.2541, ...,  0.3208, -0.6585,  0.0586]]]
+> Tensor[[1, 7, 768], f32]
+
+```

candle-examples/examples/distilbert/main.rs

@@ -0,0 +1,135 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};
+
+use anyhow::{Error as E, Result};
+use candle::{Device, Tensor};
+use candle_nn::VarBuilder;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    /// When set, compute embeddings for this prompt.
+    #[arg(long)]
+    prompt: String,
+
+    /// Use the pytorch weights rather than the safetensors ones
+    #[arg(long)]
+    use_pth: bool,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
+
+    /// L2 normalization for embeddings.
+    #[arg(long, default_value = "true")]
+    normalize_embeddings: bool,
+}
+
+impl Args {
+    fn build_model_and_tokenizer(&self) -> Result<(DistilBertModel, Tokenizer)> {
+        let device = candle_examples::device(self.cpu)?;
+        let default_model = "distilbert-base-uncased".to_string();
+        let default_revision = "main".to_string();
+        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
+            (Some(model_id), Some(revision)) => (model_id, revision),
+            (Some(model_id), None) => (model_id, "main".to_string()),
+            (None, Some(revision)) => (default_model, revision),
+            (None, None) => (default_model, default_revision),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let (config_filename, tokenizer_filename, weights_filename) = {
+            let api = Api::new()?;
+            let api = api.repo(repo);
+            let config = api.get("config.json")?;
+            let tokenizer = api.get("tokenizer.json")?;
+            let weights = if self.use_pth {
+                api.get("pytorch_model.bin")?
+            } else {
+                api.get("model.safetensors")?
+            };
+            (config, tokenizer, weights)
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let config: Config = serde_json::from_str(&config)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+        let vb = if self.use_pth {
+            VarBuilder::from_pth(&weights_filename, DTYPE, &device)?
+        } else {
+            unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
+        };
+        let model = DistilBertModel::load(vb, &config)?;
+        Ok((model, tokenizer))
+    }
+}
+
+fn get_mask(size: usize, device: &Device) -> Tensor {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device).unwrap()
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let device = &model.device;
+
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(args.prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    let mask = get_mask(tokens.len(), device);
+
+    println!("token_ids: {:?}", token_ids.to_vec2::<u32>());
+    println!("mask: {:?}", mask.to_vec2::<u8>());
+
+    let ys = model.forward(&token_ids, &mask)?;
+    println!("{ys}");
+
+    Ok(())
+}
+
+pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
+    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
+}

candle-examples/examples/llama2-c/main.rs

@@ -329,18 +329,14 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .get_ids()
         .to_vec();
 
-    println!("{tokens:?}");
-
     let start_gen = std::time::Instant::now();
-    for index in 0..1 {
+    for index in 0.. {
         if tokens.len() >= config.seq_len {
             break;
         }
         let context_size = if index > 0 { 1 } else { tokens.len() };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        // println!("Input {}", input);
-        // println!("Input {}", input.to_device(&candle::Device::Cpu)?);
         let logits = model.forward(&input, index_pos)?;
         let logits = logits.i((0, logits.dim(1)? - 1))?;
         let logits = if common_args.repeat_penalty == 1. || tokens.is_empty() {

candle-examples/examples/quantized/main.rs

@@ -53,6 +53,8 @@ enum Which {
     Zephyr7bAlpha,
     #[value(name = "7b-zephyr-b")]
     Zephyr7bBeta,
+    #[value(name = "7b-open-chat-3.5")]
+    OpenChat35,
 }
 
 impl Which {
@@ -67,8 +69,10 @@ impl Which {
             | Self::L7bCode
             | Self::L13bCode
             | Self::L34bCode => false,
-            // Zephyr is a fine tuned version of mistral and should be treated in the same way.
-            Self::Zephyr7bAlpha
+            // Zephyr and OpenChat are fine tuned versions of mistral and should be treated in the
+            // same way.
+            Self::OpenChat35
+            | Self::Zephyr7bAlpha
             | Self::Zephyr7bBeta
             | Self::Mistral7b
             | Self::Mistral7bInstruct => true,
@@ -87,10 +91,30 @@ impl Which {
             | Self::L13bCode
             | Self::L34bCode
             | Self::Mistral7b
-            | Self::Mistral7bInstruct => false,
+            | Self::Mistral7bInstruct
+            | Self::OpenChat35 => false,
             Self::Zephyr7bAlpha | Self::Zephyr7bBeta => true,
         }
     }
+
+    fn is_open_chat(&self) -> bool {
+        match self {
+            Which::L7b
+            | Which::L13b
+            | Which::L70b
+            | Which::L7bChat
+            | Which::L13bChat
+            | Which::L70bChat
+            | Which::L7bCode
+            | Which::L13bCode
+            | Which::L34bCode
+            | Which::Mistral7b
+            | Which::Mistral7bInstruct
+            | Which::Zephyr7bAlpha
+            | Which::Zephyr7bBeta => false,
+            Which::OpenChat35 => true,
+        }
+    }
 }
 
 #[derive(Parser, Debug)]
@@ -157,7 +181,9 @@ impl Args {
             Some(config) => std::path::PathBuf::from(config),
             None => {
                 let api = hf_hub::api::sync::Api::new()?;
-                let repo = if self.which.is_mistral() {
+                let repo = if self.which.is_open_chat() {
+                    "openchat/openchat_3.5"
+                } else if self.which.is_mistral() {
                     "mistralai/Mistral-7B-v0.1"
                 } else {
                     "hf-internal-testing/llama-tokenizer"
@@ -207,6 +233,7 @@ impl Args {
                     Which::Zephyr7bBeta => {
                         ("TheBloke/zephyr-7B-beta-GGUF", "zephyr-7b-beta.Q4_K_M.gguf")
                     }
+                    Which::OpenChat35 => ("TheBloke/openchat_3.5-GGUF", "openchat_3.5.Q4_K_M.gguf"),
                 };
                 let api = hf_hub::api::sync::Api::new()?;
                 let api = api.model(repo.to_string());
@@ -308,7 +335,8 @@ fn main() -> anyhow::Result<()> {
                 | Which::Zephyr7bAlpha
                 | Which::Zephyr7bBeta
                 | Which::L70b
-                | Which::L70bChat => 8,
+                | Which::L70bChat
+                | Which::OpenChat35 => 8,
             };
             ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?
         }
@@ -325,10 +353,11 @@ fn main() -> anyhow::Result<()> {
     };
 
     let mut pre_prompt_tokens = vec![];
-    loop {
+    for prompt_index in 0.. {
         let prompt_str = match &prompt {
             Prompt::One(prompt) => prompt.clone(),
             Prompt::Interactive | Prompt::Chat => {
+                let is_interactive = matches!(prompt, Prompt::Interactive);
                 print!("> ");
                 std::io::stdout().flush()?;
                 let mut prompt = String::new();
@@ -339,8 +368,14 @@ fn main() -> anyhow::Result<()> {
                         prompt.pop();
                     }
                 }
-                if args.which.is_zephyr() {
-                    format!("<|system|>\n</s>\n<|user|>\n{prompt}</s>\n<|assistant|>")
+                if args.which.is_open_chat() {
+                    format!("User: {prompt}<|end_of_turn|>Assistant: ")
+                } else if args.which.is_zephyr() {
+                    if prompt_index == 0 || is_interactive {
+                        format!("<|system|>\n</s>\n<|user|>\n{prompt}</s>\n<|assistant|>",)
+                    } else {
+                        format!("<|user|>\n{prompt}</s>\n<|assistant|>")
+                    }
                 } else if args.which.is_mistral() {
                     format!("[INST] {prompt} [/INST]")
                 } else {
@@ -385,8 +420,12 @@ fn main() -> anyhow::Result<()> {
             std::io::stdout().flush()?;
         }
 
-        let eos_token = *tos.tokenizer().get_vocab(true).get("</s>").unwrap();
-
+        let eos_token = if args.which.is_open_chat() {
+            "<|end_of_turn|>"
+        } else {
+            "</s>"
+        };
+        let eos_token = *tos.tokenizer().get_vocab(true).get(eos_token).unwrap();
         let start_post_prompt = std::time::Instant::now();
         let mut sampled = 0;
         for index in 0..to_sample {

candle-examples/examples/stable-diffusion/main.rs

@@ -416,7 +416,7 @@ fn run(args: Args) -> Result<()> {
 
     println!("Building the autoencoder.");
     let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
-    let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
+    let vae = sd_config.build_vae(vae_weights, &device, dtype)?;
     let init_latent_dist = match &img2img {
         None => None,
         Some(image) => {
@@ -426,7 +426,7 @@ fn run(args: Args) -> Result<()> {
     };
     println!("Building the unet.");
     let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
-    let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
+    let unet = sd_config.build_unet(unet_weights, &device, 4, use_flash_attn, dtype)?;
 
     let t_start = if img2img.is_some() {
         n_steps - (n_steps as f64 * img2img_strength) as usize

candle-examples/examples/t5/README.md

@@ -9,6 +9,8 @@ $ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate
 9 tokens generated (2.42 token/s)
 ```
 
+Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](https://huggingface.co/google/flan-ul2) (with `--revision "refs/pr/25"`), and [Co-EdIT](https://huggingface.co/grammarly/coedit-large) are also supported.
+
 ## Translation with [MADLAD-400](https://arxiv.org/abs/2309.04662)
 
 MADLAD-400 is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
@@ -22,7 +24,7 @@ cargo run --example t5 --release  -- \
  Wie geht es dir, mein Freund?
 ```
 
-## Sentence embedding example:
+## Sentence embedding example
 
 ```bash
 $ cargo run --example t5 --release -- --model-id "t5-small" --prompt "A beautiful candle."

candle-examples/examples/t5/main.rs

@@ -104,6 +104,17 @@ impl T5ModelBuilder {
                 api.get("model-00004-of-00005.safetensors")?,
                 api.get("model-00005-of-00005.safetensors")?,
             ]
+        } else if model_id == "google/flan-ul2" {
+            vec![
+                api.get("model-00001-of-00008.safetensors")?,
+                api.get("model-00002-of-00008.safetensors")?,
+                api.get("model-00003-of-00008.safetensors")?,
+                api.get("model-00004-of-00008.safetensors")?,
+                api.get("model-00005-of-00008.safetensors")?,
+                api.get("model-00006-of-00008.safetensors")?,
+                api.get("model-00007-of-00008.safetensors")?,
+                api.get("model-00008-of-00008.safetensors")?,
+            ]
         } else {
             vec![api.get("model.safetensors")?]
         };

candle-examples/examples/trocr/image_processor.rs

@@ -0,0 +1,154 @@
+use image::{DynamicImage, ImageBuffer};
+use serde::Deserialize;
+use std::collections::HashMap;
+
+use candle::{DType, Device, Result, Tensor};
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct ProcessorConfig {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_rescale: bool,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl Default for ProcessorConfig {
+    fn default() -> Self {
+        Self {
+            do_resize: true,
+            height: 384,
+            width: 384,
+            do_rescale: true,
+            do_normalize: true,
+            image_mean: vec![0.5, 0.5, 0.5],
+            image_std: vec![0.5, 0.5, 0.5],
+        }
+    }
+}
+
+pub struct ViTImageProcessor {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl ViTImageProcessor {
+    pub fn new(config: &ProcessorConfig) -> Self {
+        Self {
+            do_resize: config.do_resize,
+            height: config.height,
+            width: config.width,
+            do_normalize: config.do_normalize,
+            image_mean: config.image_mean.clone(),
+            image_std: config.image_std.clone(),
+        }
+    }
+
+    pub fn preprocess(&self, images: Vec<&str>) -> Result<Tensor> {
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let images = self.load_images(images)?;
+
+        let resized_images: Vec<DynamicImage> = if self.do_resize {
+            images
+                .iter()
+                .map(|image| self.resize(image.clone(), None).unwrap())
+                .collect()
+        } else {
+            images
+        };
+
+        let normalized_images: Vec<Tensor> = if self.do_normalize {
+            resized_images
+                .iter()
+                .map(|image| self.normalize(image.clone(), None, None).unwrap())
+                .collect()
+        } else {
+            let resized_images: Vec<ImageBuffer<image::Rgb<u8>, Vec<u8>>> =
+                resized_images.iter().map(|image| image.to_rgb8()).collect();
+            let data = resized_images
+                .into_iter()
+                .map(|image| image.into_raw())
+                .collect::<Vec<Vec<u8>>>();
+
+            data.iter()
+                .map(|image| {
+                    Tensor::from_vec(image.clone(), (height, width, channels), &Device::Cpu)
+                        .unwrap()
+                        .permute((2, 0, 1))
+                        .unwrap()
+                })
+                .collect::<Vec<Tensor>>()
+        };
+
+        Tensor::stack(&normalized_images, 0)
+    }
+
+    fn resize(
+        &self,
+        image: image::DynamicImage,
+        size: Option<HashMap<String, u32>>,
+    ) -> Result<image::DynamicImage> {
+        let (height, width) = match &size {
+            Some(size) => (size.get("height").unwrap(), size.get("width").unwrap()),
+            None => (&self.height, &self.width),
+        };
+
+        let resized_image =
+            image.resize_exact(*width, *height, image::imageops::FilterType::Triangle);
+
+        Ok(resized_image)
+    }
+
+    fn normalize(
+        &self,
+        image: image::DynamicImage,
+        mean: Option<Vec<f32>>,
+        std: Option<Vec<f32>>,
+    ) -> Result<Tensor> {
+        let mean = match mean {
+            Some(mean) => mean,
+            None => self.image_mean.clone(),
+        };
+
+        let std = match std {
+            Some(std) => std,
+            None => self.image_std.clone(),
+        };
+
+        let mean = Tensor::from_vec(mean, (3, 1, 1), &Device::Cpu)?;
+        let std = Tensor::from_vec(std, (3, 1, 1), &Device::Cpu)?;
+
+        let image = image.to_rgb8();
+        let data = image.into_raw();
+
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let data =
+            Tensor::from_vec(data, &[height, width, channels], &Device::Cpu)?.permute((2, 0, 1))?;
+
+        (data.to_dtype(DType::F32)? / 255.)?
+            .broadcast_sub(&mean)?
+            .broadcast_div(&std)
+    }
+
+    pub fn load_images(&self, image_path: Vec<&str>) -> Result<Vec<image::DynamicImage>> {
+        let mut images: Vec<image::DynamicImage> = Vec::new();
+        for path in image_path {
+            let img = image::io::Reader::open(path)?.decode().unwrap();
+            images.push(img);
+        }
+
+        Ok(images)
+    }
+}

candle-examples/examples/trocr/main.rs

@@ -0,0 +1,132 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::trocr;
+
+use tokenizers::Tokenizer;
+mod image_processor;
+
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    Base,
+    Large,
+}
+
+#[derive(Parser, Debug)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    /// Choose the variant of the model to run.
+    #[arg(long, default_value = "base")]
+    which: Which,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Text to be translated
+    #[arg(long)]
+    image: String,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
+    let args = Args::parse();
+
+    let tokenizer_dec = {
+        let tokenizer = Api::new()?
+            .model(String::from("ToluClassics/candle-trocr-tokenizer"))
+            .get("tokenizer.json")?;
+
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let vb = {
+        let model = match args.model {
+            Some(model) => std::path::PathBuf::from(model),
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-base-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/3".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Large => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-large-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/6".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+            },
+        };
+        println!("model: {:?}", model);
+        unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
+    };
+
+    let encoder_config = match args.which {
+        Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
+        Which::Large => {
+            candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
+        }
+    };
+
+    let decoder_config = trocr::TrOCRConfig::default();
+    let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
+
+    let config = image_processor::ProcessorConfig::default();
+    let processor = image_processor::ViTImageProcessor::new(&config);
+
+    let image = vec![args.image.as_str()];
+    let image = processor.preprocess(image)?;
+
+    let encoder_xs = model.encoder().forward(&image)?;
+
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let mut token_ids: Vec<u32> = vec![decoder_config.decoder_start_token_id];
+    for index in 0..1000 {
+        let context_size = if index >= 1 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+
+        let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
+
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        token_ids.push(token);
+
+        if let Some(t) = tokenizer_dec.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        if token == decoder_config.eos_token_id {
+            break;
+        }
+    }
+
+    if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+
+    Ok(())
+}

candle-examples/examples/trocr/readme.md

@@ -0,0 +1,16 @@
+# candle-trocr
+
+`TrOCR` is a transformer OCR Model. In this example it is used to
+transcribe image text. See the associated [model
+card](https://huggingface.co/microsoft/trocr-base-printed) for details on
+the model itself.
+
+## Running an example
+
+```bash
+cargo run --example trocr --release --  --which base --cpu --image candle-examples/examples/trocr/assets/trocr.png
+```
+
+```
+<s> industry , Mr. Brown commented icily . " Let us have a</s>
+```

candle-examples/examples/whisper/main.rs

@@ -128,7 +128,13 @@ impl Decoder {
         let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
         let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
         let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
-        let no_speech_token = token_id(&tokenizer, m::NO_SPEECH_TOKEN)?;
+        let no_speech_token = m::NO_SPEECH_TOKENS
+            .iter()
+            .find_map(|token| token_id(&tokenizer, token).ok());
+        let no_speech_token = match no_speech_token {
+            None => anyhow::bail!("unable to find any non-speech token"),
+            Some(n) => n,
+        };
         Ok(Self {
             model,
             rng: rand::rngs::StdRng::seed_from_u64(seed),
@@ -512,11 +518,7 @@ fn main() -> Result<()> {
             )
         } else {
             let config = repo.get("config.json")?;
-            let tokenizer = if args.model == WhichModel::LargeV3 {
-                panic!("openai/whisper-large-v3 does not provide a compatible tokenizer.json config at the moment")
-            } else {
-                repo.get("tokenizer.json")?
-            };
+            let tokenizer = repo.get("tokenizer.json")?;
             let model = repo.get("model.safetensors")?;
             (config, tokenizer, model)
         };

candle-examples/examples/yi/main.rs

@@ -0,0 +1,268 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::{Error as E, Result};
+use clap::{Parser, ValueEnum};
+
+use candle_transformers::models::yi::{Config, Model};
+
+use candle::{DType, Device, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
+enum Which {
+    #[value(name = "6b")]
+    L6b,
+    #[value(name = "34b")]
+    L34b,
+}
+
+struct TextGeneration {
+    model: Model,
+    device: Device,
+    tokenizer: TokenOutputStream,
+    logits_processor: LogitsProcessor,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+}
+
+impl TextGeneration {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        model: Model,
+        tokenizer: Tokenizer,
+        seed: u64,
+        temp: Option<f64>,
+        top_p: Option<f64>,
+        repeat_penalty: f32,
+        repeat_last_n: usize,
+        device: &Device,
+    ) -> Self {
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
+        Self {
+            model,
+            tokenizer: TokenOutputStream::new(tokenizer),
+            logits_processor,
+            repeat_penalty,
+            repeat_last_n,
+            device: device.clone(),
+        }
+    }
+
+    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
+        use std::io::Write;
+        self.tokenizer.clear();
+        let mut tokens = self
+            .tokenizer
+            .tokenizer()
+            .encode(prompt, true)
+            .map_err(E::msg)?
+            .get_ids()
+            .to_vec();
+        for &t in tokens.iter() {
+            if let Some(t) = self.tokenizer.next_token(t)? {
+                print!("{t}")
+            }
+        }
+        std::io::stdout().flush()?;
+
+        let mut generated_tokens = 0usize;
+        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
+            Some(token) => token,
+            None => anyhow::bail!("cannot find the <|endoftext|> token"),
+        };
+        let start_gen = std::time::Instant::now();
+        for index in 0..sample_len {
+            let context_size = if index > 0 { 1 } else { tokens.len() };
+            let start_pos = tokens.len().saturating_sub(context_size);
+            let ctxt = &tokens[start_pos..];
+            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
+            let logits = self.model.forward(&input, start_pos)?;
+            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
+            let logits = if self.repeat_penalty == 1. {
+                logits
+            } else {
+                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
+                candle_transformers::utils::apply_repeat_penalty(
+                    &logits,
+                    self.repeat_penalty,
+                    &tokens[start_at..],
+                )?
+            };
+
+            let next_token = self.logits_processor.sample(&logits)?;
+            tokens.push(next_token);
+            generated_tokens += 1;
+            if next_token == eos_token {
+                break;
+            }
+            if let Some(t) = self.tokenizer.next_token(next_token)? {
+                print!("{t}");
+                std::io::stdout().flush()?;
+            }
+        }
+        let dt = start_gen.elapsed();
+        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
+            print!("{rest}");
+        }
+        std::io::stdout().flush()?;
+        println!(
+            "\n{generated_tokens} tokens generated ({:.2} token/s)",
+            generated_tokens as f64 / dt.as_secs_f64(),
+        );
+        Ok(())
+    }
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    #[arg(long)]
+    prompt: String,
+
+    /// The temperature used to generate samples.
+    #[arg(long)]
+    temperature: Option<f64>,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// The seed to use when generating random samples.
+    #[arg(long, default_value_t = 299792458)]
+    seed: u64,
+
+    /// The length of the sample to generate (in tokens).
+    #[arg(long, short = 'n', default_value_t = 100)]
+    sample_len: usize,
+
+    #[arg(long, default_value = "01-ai/Yi-6B")]
+    model_id: String,
+
+    #[arg(long, default_value = "main")]
+    revision: String,
+
+    #[arg(long)]
+    tokenizer_file: Option<String>,
+
+    #[arg(long)]
+    weight_files: Option<String>,
+
+    /// 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 = "6b")]
+    which: Which,
+}
+
+fn main() -> 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.unwrap_or(0.),
+        args.repeat_penalty,
+        args.repeat_last_n
+    );
+
+    let start = std::time::Instant::now();
+    let api = Api::new()?;
+    let repo = api.repo(Repo::with_revision(
+        args.model_id,
+        RepoType::Model,
+        args.revision,
+    ));
+    let tokenizer_filename = match args.tokenizer_file {
+        Some(file) => std::path::PathBuf::from(file),
+        None => repo.get("tokenizer.json")?,
+    };
+    let filenames = match args.weight_files {
+        Some(files) => files
+            .split(',')
+            .map(std::path::PathBuf::from)
+            .collect::<Vec<_>>(),
+        None => match args.which {
+            Which::L6b => vec![
+                repo.get("model-00001-of-00002.safetensors")?,
+                repo.get("model-00002-of-00002.safetensors")?,
+            ],
+            Which::L34b => vec![
+                repo.get("model-00001-of-00007.safetensors")?,
+                repo.get("model-00002-of-00007.safetensors")?,
+                repo.get("model-00003-of-00007.safetensors")?,
+                repo.get("model-00004-of-00007.safetensors")?,
+                repo.get("model-00005-of-00007.safetensors")?,
+                repo.get("model-00006-of-00007.safetensors")?,
+                repo.get("model-00007-of-00007.safetensors")?,
+            ],
+        },
+    };
+    println!("retrieved the files in {:?}", start.elapsed());
+    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+    let start = std::time::Instant::now();
+    let config = match args.which {
+        Which::L6b => Config::config_6b(),
+        Which::L34b => Config::config_34b(),
+    };
+    let device = candle_examples::device(args.cpu)?;
+    let dtype = if device.is_cuda() {
+        DType::BF16
+    } else {
+        DType::F32
+    };
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
+    let model = Model::new(&config, vb)?;
+
+    println!("loaded the model in {:?}", start.elapsed());
+
+    let mut pipeline = TextGeneration::new(
+        model,
+        tokenizer,
+        args.seed,
+        args.temperature,
+        args.top_p,
+        args.repeat_penalty,
+        args.repeat_last_n,
+        &device,
+    );
+    pipeline.run(&args.prompt, args.sample_len)?;
+    Ok(())
+}

candle-examples/examples/yolo-v3/main.rs

@@ -43,6 +43,7 @@ pub fn report(
     confidence_threshold: f32,
     nms_threshold: f32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (npreds, pred_size) = pred.dims2()?;
     let nclasses = pred_size - 5;
     // The bounding boxes grouped by (maximum) class index.

candle-examples/examples/yolo-v8/README.md

@@ -32,7 +32,7 @@ Image source:
 ### Pose Estimation
 ```bash
 cargo run --example yolo-v8 --release -- \
-  candle-examples/examples/yolo-v8/assets/peoples.jpeg --task pose
+  candle-examples/examples/yolo-v8/assets/bike.jpg --task pose
 ```
 
 ![Leading group, Giro d'Italia 2021](./assets/bike.pose.jpg)

candle-examples/examples/yolo-v8/main.rs

@@ -7,7 +7,7 @@ extern crate accelerate_src;
 mod model;
 use model::{Multiples, YoloV8, YoloV8Pose};
 
-use candle::{DType, IndexOp, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::{Module, VarBuilder};
 use candle_transformers::object_detection::{non_maximum_suppression, Bbox, KeyPoint};
 use clap::{Parser, ValueEnum};
@@ -61,6 +61,7 @@ pub fn report_detect(
     nms_threshold: f32,
     legend_size: u32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (pred_size, npreds) = pred.dims2()?;
     let nclasses = pred_size - 4;
     // The bounding boxes grouped by (maximum) class index.
@@ -153,6 +154,7 @@ pub fn report_pose(
     confidence_threshold: f32,
     nms_threshold: f32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (pred_size, npreds) = pred.dims2()?;
     if pred_size != 17 * 3 + 4 + 1 {
         candle::bail!("unexpected pred-size {pred_size}");

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.3.0"
+version = "0.3.1"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], version = "0.3.0", package = "candle-core" }
+candle = { path = "../candle-core", features = ["cuda"], version = "0.3.1", package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]
@@ -21,4 +21,4 @@ rayon = "1.7.0"
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
-candle-nn = { path = "../candle-nn", version = "0.3.0", features = ["cuda"] }
+candle-nn = { path = "../candle-nn", version = "0.3.1", features = ["cuda"] }

candle-flash-attn/src/lib.rs

@@ -233,8 +233,8 @@ impl FlashAttnVarLen {
 
         let (seqlens_q, seqlens_q_layout) = self.seqlens_q.storage_and_layout();
         let seqlens_q = match &*seqlens_q {
-            candle::Storage::Cpu(_) => candle::bail!("seqlens_q must be a cuda tensor"),
             candle::Storage::Cuda(c) => c.as_cuda_slice::<u32>()?, // Should be i32!
+            _ => candle::bail!("seqlens_q must be a cuda tensor"),
         };
         let seqlens_q = match seqlens_q_layout.contiguous_offsets() {
             Some((o1, o2)) => seqlens_q.slice(o1..o2),
@@ -243,8 +243,8 @@ impl FlashAttnVarLen {
 
         let (seqlens_k, seqlens_k_layout) = self.seqlens_k.storage_and_layout();
         let seqlens_k = match &*seqlens_k {
-            candle::Storage::Cpu(_) => candle::bail!("seqlens_k must be a cuda tensor"),
             candle::Storage::Cuda(c) => c.as_cuda_slice::<u32>()?, // Should be i32!
+            _ => candle::bail!("seqlens_k must be a cuda tensor"),
         };
         let seqlens_k = match seqlens_k_layout.contiguous_offsets() {
             Some((o1, o2)) => seqlens_k.slice(o1..o2),

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.3.0"
+version = "0.3.1"
 edition = "2021"
 
 description = "CUDA kernels for Candle"
@@ -14,4 +14,4 @@ license = "MIT OR Apache-2.0"
 [build-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
 glob = "0.3.1"
-rayon = "1.7.0"
+rayon = "1.7.0"

candle-metal-kernels/Cargo.toml

@@ -1,17 +1,16 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.3.0"
+version = "0.3.1"
 edition = "2021"
 
-description = "CUDA kernels for Candle"
+description = "Metal kernels for Candle"
 repository = "https://github.com/huggingface/candle"
 keywords = ["blas", "tensor", "machine-learning"]
 categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-# metal = { git = "https://github.com/ivarflakstad/metal-rs.git", features = ["mps"] }
-metal = { path = "../../metal-rs", features = ["mps"] }
+metal = { version = "0.27.1", features = ["mps"], package="candle-metal" }
 once_cell = "1.18.0"
 thiserror = "1"
 tracing = "0.1.37"

candle-metal-kernels/src/affine.metal

@@ -29,15 +29,96 @@ kernel void FN_NAME( \
     if (id >= dim) { \
         return; \
     } \
-    const TYPENAME m = TYPENAME(mul); \
-    const TYPENAME a = TYPENAME(add); \
-    output[id] = input[id] * m + a; \
+    output[id] = TYPENAME(float(input[id]) * mul + add); \
 } \
+kernel void FN_NAME##_strided( \
+    constant size_t &dim, \
+    constant size_t &num_dims, \
+    constant size_t *dims, \
+    constant size_t *strides, \
+    constant float &mul, \
+    constant float &add, \
+    device const TYPENAME *input,  \
+    device TYPENAME *output, \
+    uint id [[ thread_position_in_grid ]] \
+) { \
+    if (id >= dim) { \
+        return; \
+    } \
+    output[id] = TYPENAME(float(input[get_strided_index(id, num_dims, dims, strides)]) * mul + add); \
+}
+
+#define POWF(FN_NAME, TYPENAME) \
+kernel void FN_NAME( \
+    constant size_t &dim, \
+    constant float &mul, \
+    device const TYPENAME *input,  \
+    device TYPENAME *output, \
+    uint id [[ thread_position_in_grid ]] \
+) { \
+    if (id >= dim) { \
+        return; \
+    } \
+    output[id] = TYPENAME(pow(input[id], TYPENAME(mul))); \
+} \
+kernel void FN_NAME##_strided( \
+    constant size_t &dim, \
+    constant size_t &num_dims, \
+    constant size_t *dims, \
+    constant size_t *strides, \
+    constant float &mul, \
+    device const TYPENAME *input,  \
+    device TYPENAME *output, \
+    uint id [[ thread_position_in_grid ]] \
+) { \
+    if (id >= dim) { \
+        return; \
+    } \
+    output[id] = TYPENAME(pow(input[get_strided_index(id, num_dims, dims, strides)], TYPENAME(mul))); \
+}
+
+#define ELU(FN_NAME, TYPENAME) \
+kernel void FN_NAME( \
+    constant size_t &dim, \
+    constant float &mul, \
+    device const TYPENAME *input,  \
+    device TYPENAME *output, \
+    uint id [[ thread_position_in_grid ]] \
+) { \
+    if (id >= dim) { \
+        return; \
+    } \
+    const TYPENAME x = input[id]; \
+    output[id] = TYPENAME((x > 0)?x: mul * exp(x - 1)); \
+} \
+kernel void FN_NAME##_strided( \
+    constant size_t &dim, \
+    constant size_t &num_dims, \
+    constant size_t *dims, \
+    constant size_t *strides, \
+    constant float &mul, \
+    device const TYPENAME *input,  \
+    device TYPENAME *output, \
+    uint id [[ thread_position_in_grid ]] \
+) { \
+    if (id >= dim) { \
+        return; \
+    } \
+    const TYPENAME x = input[get_strided_index(id, num_dims, dims, strides)]; \
+    output[id] = TYPENAME((x > 0)?x: mul * exp(x - 1)); \
+} \
+
 
 AFFINE(affine_float, float)
 AFFINE(affine_half, half)
+POWF(powf_float, float)
+POWF(powf_half, half)
+ELU(elu_float, float)
+ELU(elu_half, half)
 
 
 #if __METAL_VERSION__ >= 310
 AFFINE(affine_bfloat, bfloat);
+POWF(powf_bfloat, bfloat);
+ELU(elu_bfloat, bfloat);
 #endif

candle-metal-kernels/src/cast.metal

@@ -23,12 +23,12 @@ kernel void FN_NAME( \
     constant size_t &dim, \
     device const LEFT_TYPENAME *input,  \
     device RIGHT_TYPENAME *output, \
-    uint thread_position_in_grid [[ thread_position_in_grid ]] \
+    uint tid [[ thread_position_in_grid ]] \
 ) { \
-    if (thread_position_in_grid >= dim) { \
+    if (tid >= dim) { \
         return; \
     } \
-    output[thread_position_in_grid] = RIGHT_TYPENAME(input[thread_position_in_grid]); \
+    output[tid] = RIGHT_TYPENAME(input[tid]); \
 } \
 kernel void FN_NAME_STRIDED( \
     constant size_t &dim, \
@@ -37,15 +37,19 @@ kernel void FN_NAME_STRIDED( \
     constant size_t *strides, \
     device const LEFT_TYPENAME *input,  \
     device RIGHT_TYPENAME *output, \
-    uint i [[ thread_position_in_grid ]] \
+    uint tid [[ thread_position_in_grid ]] \
 ) { \
-    if (i >= dim) { \
+    if (tid >= dim) { \
         return; \
     } \
-    output[i] = RIGHT_TYPENAME(input[get_strided_index(i, num_dims, dims, strides)]); \
+    output[tid] = RIGHT_TYPENAME(input[get_strided_index(tid, num_dims, dims, strides)]); \
 } \
 
-CAST(cast_u32_f32, cast_u32_f32_strided, int32_t, float)
+CAST(cast_u32_f32, cast_u32_f32_strided, uint32_t, float)
+CAST(cast_u32_u8, cast_u32_u8_strided, uint32_t, uint8_t)
+CAST(cast_u8_u32, cast_u8_u32_strided, uint8_t, uint32_t)
+CAST(cast_f16_f32, cast_f16_f32_strided, half, float)
+CAST(cast_f32_f16, cast_f32_f16_strided, float, half)
 
 #if __METAL_VERSION__ >= 310
 #endif

candle-metal-kernels/src/indexing.metal

@@ -16,16 +16,16 @@ kernel void NAME( \
     if (gid >= dst_size) { \
         return; \
     } \
-    const size_t id_i = gid / right_size / left_size; \
+    const size_t id_i = (gid / right_size) % ids_size; \
+    const INDEX_TYPENAME input_i = min(input_ids[id_i], (INDEX_TYPENAME)(src_dim_size - 1)); \
     const size_t right_rank_i = gid % right_size; \
-    const size_t left_rank_i = gid % left_size; \
+    const size_t left_rank_i = gid / right_size / ids_size; \
     /* \
     // Force prevent out of bounds indexing \
     // since there doesn't seem to be a good way to force crash \
     // No need to check for zero we're only allowing unsized. \
     */ \
-    const INDEX_TYPENAME input_i = min(input_ids[id_i], (INDEX_TYPENAME)(src_dim_size - 1)); \
-    const size_t src_i = ((input_i * right_size) + right_rank_i) * left_size + left_rank_i; \
+    const size_t src_i = left_rank_i * src_dim_size * right_size + input_i * right_size + right_rank_i; \
     output[gid] = input[src_i]; \
 }
 
@@ -75,6 +75,7 @@ kernel void FN_NAME( \
 
 
 INDEX_OP(is_u32_f32, uint, float)
+INDEX_OP(is_u32_f16, uint, half)
 
 
 #if __METAL_VERSION__ >= 310

candle-metal-kernels/src/reduce.metal

@@ -1,6 +1,8 @@
 #include <metal_stdlib>
 using namespace metal;
 
+#define MAX(x, y) ((x) > (y) ? (x) : (y))
+
 METAL_FUNC uint get_strided_index(
     uint idx,
     constant size_t &num_dims,
@@ -16,18 +18,18 @@ METAL_FUNC uint get_strided_index(
     return strided_i;
 }
 
-constant int THREADGROUP_SIZE = 256;
+constant int THREADGROUP_SIZE = 2048;
 
-# define REDUCE(FN, NAME, TYPENAME) \
+# define REDUCE(FN, NAME, T) \
 kernel void NAME( \
     constant size_t &src_numel, \
     constant size_t &el_to_sum_per_block, \
-    device const TYPENAME *src,  \
-    device TYPENAME *dst, \
+    device const T *src,  \
+    device T *dst, \
     uint id [[ thread_position_in_grid ]], \
     uint tid [[ thread_index_in_threadgroup ]], \
     uint dst_id [[ threadgroup_position_in_grid ]], \
-    uint blockDim [[ threads_per_threadgroup ]] \
+    uint block_dim [[ threads_per_threadgroup ]] \
 ) { \
      \
    threadgroup float shared_memory[THREADGROUP_SIZE]; \
@@ -45,10 +47,10 @@ kernel void NAME( \
      // TODO: Fast version for the contiguous case. \
      // size_t strided_i = get_strided_index(idx, num_dims, dims, strides); \
      */ \
-     TYPENAME x = shared_memory[tid]; \
-     TYPENAME y = src[idx]; \
+     T x = shared_memory[tid]; \
+     T y = src[idx]; \
      shared_memory[tid] = FN; \
-     idx += blockDim; \
+     idx += block_dim; \
    } \
       \
    threadgroup_barrier(mem_flags::mem_none); \
@@ -56,10 +58,10 @@ kernel void NAME( \
    /* \
    // reduction in shared memory \
    */ \
-   for (uint s = blockDim / 2; s > 0; s >>= 1) { \
+   for (uint s = block_dim / 2; s > 0; s >>= 1) { \
        if (tid < s) { \
-           TYPENAME x = shared_memory[tid]; \
-           TYPENAME y = shared_memory[tid + s]; \
+           T x = shared_memory[tid]; \
+           T y = shared_memory[tid + s]; \
            shared_memory[tid] = FN; \
        } \
        threadgroup_barrier(mem_flags::mem_none); \
@@ -68,72 +70,74 @@ kernel void NAME( \
    dst[dst_id] = shared_memory[0]; \
 } \
 
-kernel void softmax_float(
-    constant size_t &src_numel,
-    constant size_t &el_to_sum_per_block,
-    device const float *src, 
-    device float *dst,
-    uint id [[ thread_position_in_grid ]],
-    uint tid [[ thread_index_in_threadgroup ]],
-    uint dst_id [[ threadgroup_position_in_grid ]],
-    uint blockDim [[ threads_per_threadgroup ]]
-) {
-    
-   threadgroup float shared_memory[THREADGROUP_SIZE];
-     
-   shared_memory[tid] = -INFINITY;
-   // Elements summed in this block range from dst_id * el_to_sum_per_block
-   // to (dst_id + 1) * el_to_sum_per_block.
-   size_t start_idx = dst_id * el_to_sum_per_block;
-   size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
-   size_t idx = start_idx + tid;
-
-   while (idx < stop_idx) {
-     // TODO: Fast version for the contiguous case.
-     shared_memory[tid] = max(shared_memory[tid], src[idx]);
-     idx += blockDim;
-   }
-     
-   threadgroup_barrier(mem_flags::mem_none);
-   
-   // reduction in shared memory
-   for (uint s = blockDim / 2; s > 0; s >>= 1) {
-       if (tid < s) {
-           shared_memory[tid] = max(shared_memory[tid], shared_memory[tid + s]);
-       }
-       threadgroup_barrier(mem_flags::mem_none);
-   }
-   
-   float max = shared_memory[0];
-
-   shared_memory[tid] = 0;
-
-   // Restart
-   idx = start_idx + tid;
-   while (idx < stop_idx) {
-     // TODO: Fast version for the contiguous case.
-     const float val = exp(src[idx] - max);
-     dst[idx] = val; 
-     shared_memory[tid] += val;
-     idx += blockDim;
-   }
-   // reduction in shared memory
-   for (uint s = blockDim / 2; s > 0; s >>= 1) {
-       if (tid < s) {
-           shared_memory[tid] += shared_memory[tid + s];
-       }
-       threadgroup_barrier(mem_flags::mem_none);
-   }
-
-   const float inv_acc = 1/shared_memory[0];
-   idx = start_idx + tid;
-   while (idx < stop_idx) {
-     dst[idx] *= inv_acc; 
-     idx += blockDim;
-   }
-}
-
 
 REDUCE(x + y, fast_sum_float, float)
 REDUCE(x * y, fast_mul_float, float)
 REDUCE(max(x, y), fast_max_float, float)
+
+#define SOFTMAX(NAME, T)                                                          \
+kernel void NAME(                                                                 \
+    constant size_t &src_numel,                                                   \
+    constant size_t &el_to_sum_per_block,                                         \
+    device const T *src,                                                          \
+    device T *dst,                                                                \
+                                                                                  \
+    uint id [[ thread_position_in_grid ]],                                        \
+    uint tid [[ thread_index_in_threadgroup ]],                                   \
+    uint dst_id [[ threadgroup_position_in_grid ]],                               \
+    uint block_dim [[ threads_per_threadgroup ]]                                  \
+) {                                                                               \
+    threadgroup float shared_memory[THREADGROUP_SIZE];                                \
+    shared_memory[tid] = -INFINITY;                                            \
+    size_t start_idx = dst_id * el_to_sum_per_block;                              \
+    size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);            \
+    size_t idx = start_idx + tid;                                                 \
+                                                                                  \
+    threadgroup_barrier(mem_flags::mem_threadgroup);                              \
+                                                                                  \
+    while (idx < stop_idx) {                                                      \
+        shared_memory[tid] = MAX(shared_memory[tid], src[idx]);                   \
+        idx += block_dim;                                                         \
+    }                                                                             \
+                                                                                  \
+    threadgroup_barrier(mem_flags::mem_threadgroup);                              \
+                                                                                  \
+    for (uint s = block_dim / 2; s > 0; s >>= 1) {                                \
+        if (tid < s) {                                                            \
+            shared_memory[tid] = MAX(shared_memory[tid], shared_memory[tid + s]); \
+        }                                                                         \
+    }                                                                             \
+                                                                                  \
+    threadgroup_barrier(mem_flags::mem_threadgroup);                              \
+                                                                                  \
+    float _max = shared_memory[0];                                                    \
+                                                                                  \
+    shared_memory[tid] = 0;                                                       \
+                                                                                  \
+    idx = start_idx + tid;                                                        \
+    while (idx < stop_idx) {                                                      \
+        const T val = T(exp(src[idx] - _max));                                    \
+        dst[idx] = val;                                                           \
+        shared_memory[tid] += val;                                                \
+        idx += block_dim;                                                         \
+    }                                                                             \
+    for (uint s = block_dim / 2; s > 0; s >>= 1) {                                \
+        if (tid < s) {                                                            \
+            shared_memory[tid] += shared_memory[tid + s];                         \
+        }                                                                         \
+        threadgroup_barrier(mem_flags::mem_threadgroup);                          \
+    }                                                                             \
+                                                                                  \
+    const T inv_acc = T(1/shared_memory[0]);                                         \
+    idx = start_idx + tid;                                                        \
+    while (idx < stop_idx) {                                                      \
+        dst[idx] *= inv_acc;                                                      \
+        idx += block_dim;                                                         \
+    }                                                                             \
+}                                                                                 \
+
+SOFTMAX(softmax_float, float)
+SOFTMAX(softmax_half, half)
+#if __METAL_VERSION__ >= 310
+SOFTMAX(softmax_bfloat, bfloat)
+#endif

candle-metal-kernels/src/ternary.metal

@@ -32,6 +32,9 @@ kernel void FN_NAME(  \
     device TYPENAME *out ,\
     uint i [[ thread_position_in_grid ]] \
 ) {  \
+   if (i >= numel){ \
+       return; \
+   } \
    uint strided_i = get_strided_index(i, num_dims, dims, strides); \
    uint strided_i_t = get_strided_index(i, num_dims, dims, strides_t); \
    uint strided_i_f = get_strided_index(i, num_dims, dims, strides_f); \

candle-metal-kernels/src/tests.rs

@@ -0,0 +1,746 @@
+use super::*;
+use half::{bf16, f16};
+use metal::{CompileOptions, Device, MTLResourceOptions, MTLSize, NSUInteger};
+
+fn new_buffer<T>(device: &Device, data: &[T]) -> Buffer {
+    let options = MTLResourceOptions::StorageModeManaged;
+    let ptr = data.as_ptr() as *const core::ffi::c_void;
+    let size = (data.len() * std::mem::size_of::<T>()) as u64;
+    device.new_buffer_with_data(ptr, size, options)
+}
+
+fn device() -> Device {
+    Device::system_default().unwrap()
+}
+
+fn approx(v: Vec<f32>, digits: i32) -> Vec<f32> {
+    let b = 10f32.powi(digits);
+    v.iter().map(|t| f32::round(t * b) / b).collect()
+}
+
+fn approx_f16(v: Vec<f16>, digits: i32) -> Vec<f32> {
+    let b = 10f32.powi(digits);
+    v.iter().map(|t| f32::round(t.to_f32() * b) / b).collect()
+}
+
+fn approx_bf16(v: Vec<bf16>, digits: i32) -> Vec<f32> {
+    let b = 10f32.powi(digits);
+    v.iter().map(|t| f32::round(t.to_f32() * b) / b).collect()
+}
+
+fn run<T: Clone>(v: &[T], name: unary::contiguous::Kernel) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let input = new_buffer(&device, v);
+    let output = new_buffer(&device, v);
+    call_unary_contiguous(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        v.len(),
+        &input,
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+    output.read_to_vec::<T>(v.len())
+}
+
+fn run_binary<T: Clone>(x: &[T], y: &[T], name: binary::contiguous::Kernel) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let options = MTLResourceOptions::StorageModeManaged;
+    let left = new_buffer(&device, x);
+    let right = new_buffer(&device, y);
+    let output = device.new_buffer(std::mem::size_of_val(x) as u64, options);
+    call_binary_contiguous(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        x.len(),
+        &left,
+        &right,
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+    output.read_to_vec::<T>(x.len())
+}
+
+fn run_strided<T: Clone>(
+    v: &[T],
+    kernel: unary::strided::Kernel,
+    shape: &[usize],
+    strides: &[usize],
+    offset: usize,
+) -> Vec<T> {
+    let device = device();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let input = new_buffer(&device, v);
+    let output = new_buffer(&device, v);
+    let kernels = Kernels::new();
+    call_unary_strided(
+        &device,
+        command_buffer,
+        &kernels,
+        kernel,
+        shape,
+        &input,
+        strides,
+        offset,
+        &output,
+        0,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+    output.read_to_vec::<T>(v.len())
+}
+
+#[test]
+fn cos_f32() {
+    let v = vec![1.0f32, 2.0, 3.0];
+    let results = run(&v, unary::contiguous::cos::FLOAT);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(approx(results, 4), vec![0.5403, -0.4161, -0.99]);
+    assert_eq!(approx(expected, 4), vec![0.5403, -0.4161, -0.99]);
+
+    let v = vec![1.0f32; 10_000];
+    let results = run(&v, unary::contiguous::cos::FLOAT);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(approx(results, 4), vec![0.5403; 10_000]);
+    assert_eq!(approx(expected, 4), vec![0.5403; 10_000]);
+}
+
+#[test]
+fn cos_f32_strided() {
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let shape = vec![6];
+    let strides = vec![1];
+    let offset = 0;
+    let results = run_strided(&v, unary::strided::cos::FLOAT, &shape, &strides, offset);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(
+        approx(results, 4),
+        vec![0.5403, -0.4161, -0.99, -0.6536, 0.2837, 0.9602]
+    );
+    assert_eq!(
+        approx(expected, 4),
+        vec![0.5403, -0.4161, -0.99, -0.6536, 0.2837, 0.9602]
+    );
+
+    // Contiguous
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let shape = vec![3, 2];
+    let strides = vec![2, 1];
+    let offset = 0;
+    let results = run_strided(&v, unary::strided::cos::FLOAT, &shape, &strides, offset);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(
+        approx(results, 4),
+        vec![0.5403, -0.4161, -0.99, -0.6536, 0.2837, 0.9602]
+    );
+    assert_eq!(
+        approx(expected, 4),
+        vec![0.5403, -0.4161, -0.99, -0.6536, 0.2837, 0.9602]
+    );
+
+    // Transposed
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let shape = vec![3, 2];
+    let strides = vec![1, 3];
+    let offset = 0;
+    let results = run_strided(&v, unary::strided::cos::FLOAT, &shape, &strides, offset);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(
+        approx(results, 4),
+        vec![0.5403, -0.6536, -0.4161, 0.2837, -0.99, 0.9602]
+    );
+    assert_eq!(
+        approx(expected, 4),
+        vec![0.5403, -0.4161, -0.99, -0.6536, 0.2837, 0.9602]
+    );
+
+    // Very large
+    let v = vec![1.0f32; 10_000];
+    let shape = vec![2, 5_000];
+    let strides = vec![2, 1];
+    let offset = 0;
+    let results = run_strided(&v, unary::strided::cos::FLOAT, &shape, &strides, offset);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(approx(results, 4), vec![0.5403; 10_000]);
+    assert_eq!(approx(expected, 4), vec![0.5403; 10_000]);
+}
+
+#[test]
+fn cos_strided_random() {
+    let v: Vec<_> = (0..10_000).map(|_| rand::random::<f32>()).collect();
+    let shape = vec![5_000, 2];
+    let strides = vec![1, 5_000];
+    let offset = 0;
+    let results = run_strided(&v, unary::strided::cos::FLOAT, &shape, &strides, offset);
+    let expected: Vec<_> = v.iter().map(|v| v.cos()).collect();
+    assert_eq!(approx(vec![results[0]], 4), approx(vec![expected[0]], 4));
+    assert_eq!(
+        approx(vec![results[1]], 4),
+        approx(vec![expected[5_000]], 4)
+    );
+    assert_eq!(approx(vec![results[2]], 4), approx(vec![expected[1]], 4));
+    assert_eq!(
+        approx(vec![results[3]], 4),
+        approx(vec![expected[5_001]], 4)
+    );
+    assert_eq!(
+        approx(vec![results[5_000]], 4),
+        approx(vec![expected[2_500]], 4)
+    );
+}
+
+#[test]
+fn gelu_f16() {
+    let v: Vec<f16> = [-10f32, -1.0, 0., 1., 2., 3., 10.0, 20.0]
+        .iter()
+        .map(|v| f16::from_f32(*v))
+        .collect();
+    let expected: Vec<f32> = vec![-0.0, -0.16, 0.0, 0.84, 1.96, 3.0, 10.0, 20.0];
+    let results = run(&v, unary::contiguous::gelu::HALF);
+    assert_eq!(approx_f16(results, 2), expected);
+}
+
+#[test]
+fn gelu_f32() {
+    let v: Vec<f32> = vec![-10f32, -1.0, 0., 1., 2., 3., 10.0, 20.0];
+    let expected: Vec<f32> = vec![-0.0, -0.159, 0.0, 0.841, 1.955, 2.996, 10.0, 20.0];
+    let results = run(&v, unary::contiguous::gelu::FLOAT);
+    assert_eq!(approx(results, 3), expected);
+}
+
+#[test]
+fn binary_add_f32() {
+    let left = vec![1.0f32, 2.0, 3.0];
+    let right = vec![2.0f32, 3.1, 4.2];
+    let results = run_binary(&left, &right, binary::contiguous::add::FLOAT);
+    let expected: Vec<_> = left
+        .iter()
+        .zip(right.iter())
+        .map(|(&x, &y)| x + y)
+        .collect();
+    assert_eq!(approx(results, 4), vec![3.0f32, 5.1, 7.2]);
+    assert_eq!(approx(expected, 4), vec![3.0f32, 5.1, 7.2]);
+}
+
+fn cast<T: Clone, U: Clone>(v: &[T], name: &'static str) -> Vec<U> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let input = new_buffer(&device, v);
+    let options = MTLResourceOptions::StorageModeManaged;
+    let size = (v.len() * std::mem::size_of::<U>()) as u64;
+    let output = device.new_buffer(size, options);
+
+    call_cast_contiguous(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        v.len(),
+        &input,
+        0,
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+    output.read_to_vec::<U>(v.len())
+}
+
+#[test]
+fn cast_u32_f32() {
+    let v = vec![1u32, 2, 3];
+    let results = cast(&v, "cast_u32_f32");
+    let expected: Vec<_> = v.iter().map(|&v| v as f32).collect();
+    assert_eq!(approx(results, 4), vec![1.0f32, 2.0, 3.0]);
+    assert_eq!(approx(expected, 4), vec![1.0f32, 2.0, 3.0]);
+
+    let v = vec![1.0f32, 2.0, 3.0];
+    let input: Vec<f16> = v.iter().map(|v| f16::from_f32(*v)).collect();
+    let results: Vec<f32> = cast(&input, "cast_f16_f32");
+    assert_eq!(results, vec![1.0f32, 2.0, 3.0]);
+
+    let v = vec![1.0f32; 10_000];
+    let input: Vec<f16> = v.iter().map(|v| f16::from_f32(*v)).collect();
+    let results: Vec<f32> = cast(&input, "cast_f16_f32");
+    assert_eq!(results.len(), 10_000);
+    assert_eq!(&results[..10], vec![1.0f32; 10]);
+    assert_eq!(results, vec![1.0f32; 10_000]);
+}
+
+fn run_affine<T: Clone>(v: &[T], mul: f64, add: f64) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+
+    let input = new_buffer(&device, v);
+    let output = new_buffer(&device, v);
+
+    let size = v.len();
+
+    call_affine(
+        &device,
+        command_buffer,
+        &kernels,
+        "affine_float",
+        size,
+        &input,
+        &output,
+        mul as f32,
+        add as f32,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    output.read_to_vec::<T>(v.len())
+}
+
+fn run_affine_strided<T: Clone>(
+    v: &[T],
+    shape: &[usize],
+    strides: &[usize],
+    mul: f64,
+    add: f64,
+) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+
+    let input = new_buffer(&device, v);
+    let output = new_buffer(&device, v);
+
+    call_affine_strided(
+        &device,
+        command_buffer,
+        &kernels,
+        "affine_float_strided",
+        shape,
+        &input,
+        strides,
+        0,
+        &output,
+        mul as f32,
+        add as f32,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    let len: usize = shape.iter().product();
+    output.read_to_vec::<T>(len)
+}
+
+#[test]
+fn affine() {
+    let input = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
+    let mul = 1.5;
+    let add = 1.1;
+    let result = run_affine(&input, mul, add);
+    assert_eq!(result, vec![2.6, 4.1, 5.6, 7.1, 8.6, 10.1, 11.6, 13.1]);
+
+    let input = [1.0f32; 40_000];
+    let mul = 1.5;
+    let add = 1.1;
+    let result = run_affine(&input, mul, add);
+    assert_eq!(result, vec![2.6; 40_000]);
+}
+
+#[test]
+fn affine_strided() {
+    let input = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
+    let mul = 1.5;
+    let add = 1.1;
+    let shape = [4];
+    let strides = [2];
+    let result = run_affine_strided(&input, &shape, &strides, mul, add);
+    // 1 on 2
+    assert_eq!(result, vec![2.6, 5.6, 8.6, 11.6]);
+}
+
+#[test]
+fn index_select() {
+    let embedding = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
+    let shape = [5, 2];
+    let ids = [0u32, 4, 2];
+    let dim = 0;
+    let result = run_index_select(&embedding, &shape, &ids, dim);
+    assert_eq!(result, vec![1.0f32, 2.0, 9.0, 10.0, 5.0, 6.0]);
+
+    let embedding = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
+    let shape = [2, 5];
+    let ids = [0u32, 1, 0];
+    let dim = 0;
+    let result = run_index_select(&embedding, &shape, &ids, dim);
+    assert_eq!(
+        result,
+        vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 1.0f32, 2.0, 3.0, 4.0, 5.0]
+    );
+}
+
+#[test]
+fn index_select_f16() {
+    let embedding: Vec<_> = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+        .into_iter()
+        .map(|x| f16::from_f32(x))
+        .collect();
+    let shape = [5, 2];
+    let ids = [0u32, 4, 2];
+    let dim = 0;
+    let result = run_index_select(&embedding, &shape, &ids, dim);
+    assert_eq!(
+        approx_f16(result, 4),
+        vec![1.0f32, 2.0, 9.0, 10.0, 5.0, 6.0]
+    );
+}
+
+#[test]
+fn index_select_dim1() {
+    let embedding = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
+    let shape = [5, 2];
+    let ids = [0u32, 1, 0];
+    let dim = 1;
+    let result = run_index_select(&embedding, &shape, &ids, dim);
+    assert_eq!(
+        result,
+        vec![1.0f32, 2.0, 1.0, 3.0, 4.0, 3.0, 5.0, 6.0, 5.0, 7.0, 8.0f32, 7.0, 9.0, 10.0, 9.0]
+    );
+}
+
+fn run_index_select<T: Clone, I: Clone + std::fmt::Debug>(
+    embeddings: &[T],
+    shape: &[usize],
+    ids: &[I],
+    dim: usize,
+) -> Vec<T> {
+    let device = Device::system_default().expect("no device found");
+
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let embeddings_buffer = new_buffer(&device, &embeddings);
+    let ids_buffer = new_buffer(&device, &ids);
+
+    let left_size: usize = shape[..dim].iter().product();
+    let right_size: usize = shape[dim + 1..].iter().product();
+    let dst_el = ids.len() * left_size * right_size;
+    let dst_buffer = new_buffer(&device, &vec![0.0f32; dst_el]);
+
+    let name = match core::mem::size_of::<T>() {
+        4 => "is_u32_f32",
+        2 => "is_u32_f16",
+        _ => unimplemented!(),
+    };
+
+    let kernels = Kernels::new();
+    call_index_select(
+        &device,
+        &command_buffer,
+        &kernels,
+        name,
+        shape,
+        ids.len(),
+        dim,
+        &embeddings_buffer,
+        &ids_buffer,
+        &dst_buffer,
+    )
+    .unwrap();
+
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    dst_buffer.read_to_vec::<T>(dst_el)
+}
+
+#[test]
+fn index_add() {
+    let device = Device::system_default().expect("no device found");
+
+    let options = CompileOptions::new();
+    let library = device.new_library_with_source(INDEXING, &options).unwrap();
+
+    let left = [1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
+    let right = [1.0f32; 15];
+    let index = [0u32, 4, 2];
+    let ids_dim_size = index.len() as u32;
+    let dst_dim_size: u32 = 15;
+    let left_size: u32 = 3;
+    let right_size: u32 = 3;
+
+    let function = library.get_function("ia_u32_f32", None).unwrap();
+    let pipeline = device
+        .new_compute_pipeline_state_with_function(&function)
+        .unwrap();
+
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let encoder = command_buffer.new_compute_command_encoder();
+
+    encoder.set_compute_pipeline_state(&pipeline);
+
+    let index_buffer = new_buffer(&device, &index);
+    let inputs_buffer = new_buffer(&device, &left);
+    let outputs_buffer = new_buffer(&device, &right);
+
+    set_params!(
+        encoder,
+        (
+            &index_buffer,
+            &inputs_buffer,
+            &outputs_buffer,
+            ids_dim_size,
+            left_size,
+            dst_dim_size,
+            right_size
+        )
+    );
+
+    let grid_size = MTLSize {
+        width: right.len() as NSUInteger,
+        height: 1,
+        depth: 1,
+    };
+
+    let thread_group_size = MTLSize {
+        width: pipeline.max_total_threads_per_threadgroup(),
+        height: 1,
+        depth: 1,
+    };
+
+    encoder.dispatch_thread_groups(grid_size, thread_group_size);
+    encoder.end_encoding();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    let expected = vec![
+        2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 8.0, 9.0, 10.0, 1.0, 1.0, 1.0, 5.0, 6.0, 7.0,
+    ];
+    let result = outputs_buffer.read_to_vec::<f32>(right.len());
+    assert_eq!(result, expected);
+}
+
+#[test]
+fn cos_f16() {
+    let v: Vec<f16> = [1.0f32, 2.0, 3.0]
+        .iter()
+        .map(|v| f16::from_f32(*v))
+        .collect();
+    let results = run(&v, unary::contiguous::cos::HALF);
+    let expected: Vec<f16> = v.iter().map(|v| f16::from_f32(v.to_f32().cos())).collect();
+    assert_eq!(approx_f16(results, 2), vec![0.54, -0.42, -0.99]);
+    assert_eq!(approx_f16(expected, 2), vec![0.54, -0.42, -0.99]);
+}
+
+fn run_reduce<T: Clone>(v: &[T], out_length: usize, name: &'static str) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let input = new_buffer(&device, v);
+
+    let options = MTLResourceOptions::StorageModeManaged;
+    let output = device.new_buffer((out_length * core::mem::size_of::<T>()) as u64, options);
+    call_reduce_contiguous(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        v.len(),
+        out_length,
+        &input,
+        0,
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    output.read_to_vec::<T>(out_length)
+}
+
+fn run_softmax<T: Clone + std::fmt::Debug>(v: &[T], last_dim: usize, name: &'static str) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let input = new_buffer(&device, v);
+    let output = new_buffer(&device, v);
+    call_last_softmax(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        v.len(),
+        last_dim,
+        &input,
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    output.read_to_vec::<T>(v.len())
+}
+
+#[test]
+fn reduce_sum() {
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let out_length = 1;
+
+    let results = run_reduce(&v, out_length, "fast_sum_float");
+    assert_eq!(approx(results, 4), vec![21.0]);
+}
+
+#[test]
+fn reduce_sum2() {
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let out_length = 2;
+
+    let results = run_reduce(&v, out_length, "fast_sum_float");
+    assert_eq!(approx(results, 4), vec![6.0, 15.0]);
+}
+
+#[test]
+fn softmax() {
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let last_dim = 6;
+    let results = run_softmax(&v, last_dim, "softmax_float");
+    assert_eq!(
+        approx(results, 4),
+        vec![0.0043, 0.0116, 0.0315, 0.0858, 0.2331, 0.6337]
+    );
+
+    let v = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0];
+    let last_dim = 6;
+    let results = run_softmax(&v, last_dim, "softmax_float");
+    assert_eq!(
+        approx(results, 4),
+        vec![0.0043, 0.0116, 0.0315, 0.0858, 0.2331, 0.6337]
+    );
+
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let last_dim = 3;
+    let results = run_softmax(&v, last_dim, "softmax_float");
+    assert_eq!(
+        approx(results, 4),
+        vec![0.0900, 0.2447, 0.6652, 0.0900, 0.2447, 0.6652]
+    );
+
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0]
+        .iter()
+        .map(|v| f16::from_f32(*v))
+        .collect::<Vec<_>>();
+    let last_dim = 6;
+    let results = run_softmax(&v, last_dim, "softmax_half");
+    assert_eq!(
+        approx_f16(results, 4),
+        vec![0.0043, 0.0116, 0.0316, 0.0858, 0.2332, 0.6338]
+    );
+
+    let v = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0]
+        .iter()
+        .map(|v| bf16::from_f32(*v))
+        .collect::<Vec<_>>();
+    let last_dim = 6;
+    let results = run_softmax(&v, last_dim, "softmax_bfloat");
+    assert_eq!(
+        approx_bf16(results, 4),
+        vec![0.0043, 0.0116, 0.0315, 0.0859, 0.2324, 0.6328]
+    );
+}
+
+fn run_where_cond<I: Clone, T: Clone>(
+    shape: &[usize],
+    cond: &[I],
+    (cond_stride, cond_offset): (Vec<usize>, usize),
+    left_true: &[T],
+    (left_stride, left_offset): (Vec<usize>, usize),
+    right_false: &[T],
+    (_right_stride, _right_offset): (Vec<usize>, usize),
+    name: &'static str,
+) -> Vec<T> {
+    let device = device();
+    let kernels = Kernels::new();
+    let command_queue = device.new_command_queue();
+    let command_buffer = command_queue.new_command_buffer();
+    let options = MTLResourceOptions::StorageModeManaged;
+
+    let length = cond.len();
+    let cond = device.new_buffer_with_data(
+        cond.as_ptr() as *const core::ffi::c_void,
+        std::mem::size_of_val(cond) as u64,
+        options,
+    );
+    let left = device.new_buffer_with_data(
+        left_true.as_ptr() as *const core::ffi::c_void,
+        (length * core::mem::size_of::<T>()) as u64,
+        options,
+    );
+    let right = device.new_buffer_with_data(
+        right_false.as_ptr() as *const core::ffi::c_void,
+        (length * core::mem::size_of::<T>()) as u64,
+        options,
+    );
+
+    let output = device.new_buffer((length * core::mem::size_of::<T>()) as u64, options);
+    call_where_cond_strided(
+        &device,
+        command_buffer,
+        &kernels,
+        name,
+        shape,
+        &cond,
+        (&cond_stride, cond_offset),
+        &left,
+        (&left_stride, left_offset),
+        &right,
+        (&cond_stride, cond_offset),
+        &output,
+    )
+    .unwrap();
+    command_buffer.commit();
+    command_buffer.wait_until_completed();
+
+    output.read_to_vec::<T>(length)
+}
+
+#[test]
+fn where_cond() {
+    let shape = vec![6];
+    let cond = vec![0u8, 1, 0, 0, 1, 1];
+    let cond_l = (vec![1], 0);
+    let left_true = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
+    let left_l = (vec![1], 0);
+    let right_false = vec![-1.0f32, -2.0, -3.0, -4.0, -5.0, -6.0];
+    let right_l = (vec![1], 0);
+    let results = run_where_cond(
+        &shape,
+        &cond,
+        cond_l,
+        &left_true,
+        left_l,
+        &right_false,
+        right_l,
+        "where_u8_f32",
+    );
+    assert_eq!(approx(results, 4), vec![-1.0f32, 2.0, -3.0, -4.0, 5.0, 6.0]);
+}

candle-metal-kernels/src/unary.metal

@@ -1,4 +1,7 @@
 #include <metal_stdlib>
+#include <metal_math>
+#
+using namespace metal;
 
 METAL_FUNC uint get_strided_index(
     uint idx,
@@ -17,10 +20,44 @@ METAL_FUNC uint get_strided_index(
 
 template <typename T> METAL_FUNC T sqr(T in){ return in * in; }
 template <typename T> METAL_FUNC T neg(T in){ return -in; }
-template <typename T> METAL_FUNC T id(T in){ return in; }
+template <typename T> METAL_FUNC T erf(T in){
+    float x = (float) in;
+    // constants
+    float a1 =  0.254829592;
+    float a2 = -0.284496736;
+    float a3 =  1.421413741;
+    float a4 = -1.453152027;
+    float a5 =  1.061405429;
+    float p  =  0.3275911;
+
+    // Save the sign of x
+    int sign = 1;
+    if (x < 0)
+        sign = -1;
+    x = fabs(x);
+
+    // A&S formula 7.1.26
+    float t = 1.0/(1.0 + p*x);
+    float y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);
+
+    return T(sign*y);
+}
+template <typename T> METAL_FUNC T id(T in) { return in; }
+template <typename T> METAL_FUNC T gelu_erf(T x) {
+    return T(x * (1 + erf(x * M_SQRT1_2_F)) / 2);
+}
+template <typename T> METAL_FUNC T gelu(T x) {
+    if (x > 5) {
+        return x;
+    }
+    T x_sq = x * x;
+    T x_cube = x_sq * x;
+    T alpha = x + static_cast<T>(0.044715) * x_cube;
+    T beta =  (static_cast<T>(M_2_SQRTPI_F * M_SQRT1_2_F) * alpha);
+    return static_cast<T>(0.5) * x * (static_cast<T>(1.0) + T(tanh(beta)));
+}
 
 
-using namespace metal;
 
 #define UNARY(FN, TYPENAME, FN_NAME, FN_NAME_STRIDED) \
 kernel void FN_NAME( \
@@ -32,7 +69,7 @@ kernel void FN_NAME( \
     if (thread_position_in_grid >= dim) { \
         return; \
     } \
-    output[thread_position_in_grid] = TYPENAME(FN(input[thread_position_in_grid])); \
+    output[thread_position_in_grid] = TYPENAME(FN(float(input[thread_position_in_grid]))); \
 }\
 kernel void FN_NAME_STRIDED( \
     constant size_t &dim, \
@@ -46,7 +83,7 @@ kernel void FN_NAME_STRIDED( \
     if (thread_position_in_grid >= dim) { \
         return; \
     } \
-    output[thread_position_in_grid] = TYPENAME(FN(input[get_strided_index(thread_position_in_grid, num_dims, dims, strides)])); \
+    output[thread_position_in_grid] = TYPENAME(FN(float(input[get_strided_index(thread_position_in_grid, num_dims, dims, strides)]))); \
 }
 
 #define UNARY_OP(NAME) \
@@ -63,8 +100,18 @@ UNARY_OP(sqr)
 UNARY_OP(sqrt)
 UNARY_OP(neg)
 UNARY_OP(exp)
+UNARY_OP(log)
+UNARY_OP(gelu)
+UNARY_OP(ceil)
+UNARY_OP(floor)
+UNARY_OP(round)
+UNARY_OP(gelu_erf)
+UNARY_OP(erf)
+UNARY_OP(tanh)
 UNARY(id, float, copy_float, copy_float_strided)
 UNARY(id, half, copy_half, copy_half_strided)
+UNARY(id, uint8_t, copy_u8, copy_u8_strided)
+UNARY(id, uint32_t, copy_u32, copy_u32_strided)
 
 #if __METAL_VERSION__ >= 310
 BFLOAT_UNARY_OP(cos)
@@ -73,6 +120,14 @@ BFLOAT_UNARY_OP(sqr)
 BFLOAT_UNARY_OP(sqrt)
 BFLOAT_UNARY_OP(neg)
 BFLOAT_UNARY_OP(exp)
+BFLOAT_UNARY_OP(log)
+BFLOAT_UNARY_OP(gelu)
+BFLOAT_UNARY_OP(ceil)
+BFLOAT_UNARY_OP(floor)
+BFLOAT_UNARY_OP(round)
+BFLOAT_UNARY_OP(gelu_erf)
+BFLOAT_UNARY_OP(erf)
+BFLOAT_UNARY_OP(tanh)
 
 UNARY(id, bfloat, copy_bfloat, copy_bfloat_strided)
 #endif

candle-metal-kernels/tmp/affine.rs

@@ -50,6 +50,7 @@ fn run_affine_bench<T: Clone>(device: &Device, kernels: &Kernels, v: &[T]) {
                 &device,
                 command_buffer,
                 &kernels,
+                "affine_float",
                 v.len(),
                 &input,
                 &mut output,

candle-metal-kernels/tmp/unary.rs

@@ -147,7 +147,7 @@ fn run_unary_bench<T: Clone>(
         println!(
             "{0: <5} | {1: <19} | {2: <6} | {3: <5} | {4: <11?} | {5: <11?}",
             type_name::<T>().split("::").last().unwrap(),
-            kernel_name.to_string(),
+            kernel_name.0,
             v.len(),
             iterations,
             total_time,
@@ -159,7 +159,7 @@ fn run_unary_bench<T: Clone>(
     let shape = vec![2, 5_000];
     let strides = vec![2, 1];
     let offset = 0;
-    for kernel_name in strided {
+    for kernel_name in &strided {
         let total_time = autoreleasepool(|| {
             let command_buffer = command_queue.new_command_buffer();
             let start = Instant::now();
@@ -187,7 +187,7 @@ fn run_unary_bench<T: Clone>(
         println!(
             "{0: <5} | {1: <19} | {2: <6} | {3: <5} | {4: <11?} | {5: <11?}",
             type_name::<T>().split("::").last().unwrap(),
-            kernel_name.to_string(),
+            kernel_name.0,
             v.len(),
             iterations,
             total_time,

candle-nn/Cargo.toml

@@ -11,7 +11,7 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
 half = { workspace = true }
 thiserror = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
@@ -19,6 +19,8 @@ num-traits = { workspace = true }
 rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
+metal = { workspace = true, optional = true }
+candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.0", optional = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
@@ -29,3 +31,4 @@ default = []
 accelerate = ["dep:accelerate-src", "candle/accelerate"]
 cuda = ["candle/cuda"]
 mkl = ["dep:intel-mkl-src", "candle/mkl"]
+metal = ["candle/metal", "dep:candle-metal-kernels", "dep:metal"]

candle-nn/examples/cpu_benchmarks.rs

@@ -6,7 +6,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle::quantized::GgmlType;
-use candle::{CpuStorage, Device, Layout, Result, Shape, Tensor, D};
+use candle::{CpuStorage, Device, Layout, Module, Result, Shape, Tensor, D};
 use clap::{Parser, Subcommand};
 
 const CHECK_CONV2D: bool = false;

candle-nn/src/activation.rs

@@ -6,7 +6,6 @@ use serde::Deserialize;
 pub enum Activation {
     #[default]
     Gelu,
-    #[serde(rename = "gated-gelu")]
     NewGelu,
     Relu,
     Relu2,

candle-nn/src/embedding.rs

@@ -9,7 +9,6 @@ pub struct Embedding {
 
 impl Embedding {
     pub fn new(embeddings: Tensor, hidden_size: usize) -> Self {
-        // todo!("Embedding {embeddings}");
         Self {
             embeddings,
             hidden_size,

candle-nn/src/ops.rs

@@ -201,6 +201,48 @@ impl candle::CustomOp1 for SoftmaxLastDim {
         };
         Ok((dst, layout.shape().clone()))
     }
+
+    #[cfg(feature = "metal")]
+    fn metal_fwd(
+        &self,
+        storage: &candle::MetalStorage,
+        layout: &Layout,
+    ) -> Result<(candle::MetalStorage, Shape)> {
+        use candle::{backend::BackendStorage, DType};
+        let device = storage.device();
+        let command_buffer = device.command_buffer();
+        let kernels = device.kernels();
+        let name = match storage.dtype() {
+            DType::F32 => "softmax_float",
+            DType::F16 => "softmax_half",
+            DType::BF16 => "softmax_bfloat",
+            dtype => candle::bail!("softmax-last-dim is not implemented for {dtype:?}"),
+        };
+
+        let n = layout.stride().len();
+        if !(layout.stride()[n - 1] == 1 && layout.start_offset() == 0) {
+            candle::bail!("Non contiguous softmax-last-dim is not implemented");
+        }
+
+        let last_dim = layout.dims()[layout.shape().rank() - 1];
+        let elem_count = layout.shape().elem_count();
+        let mut output = device.new_buffer(elem_count, storage.dtype(), "softmax");
+        candle_metal_kernels::call_last_softmax(
+            device.metal_device(),
+            &command_buffer,
+            &kernels,
+            name,
+            elem_count,
+            last_dim,
+            storage.buffer(),
+            &mut output,
+        )
+        .unwrap();
+        command_buffer.commit();
+        output.did_modify_range(metal::NSRange::new(0, output.length()));
+        let newstorage = candle::MetalStorage::new(output, device.clone(), storage.dtype());
+        Ok((newstorage, layout.shape().clone()))
+    }
 }
 
 pub fn softmax_last_dim(xs: &Tensor) -> Result<Tensor> {

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.3.0"
+version = "0.3.1"
 edition = "2021"
 
 description = "ONNX support for Candle"
@@ -10,8 +10,8 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
 prost = "0.12.1"
 
 [build-dependencies]

candle-onnx/src/eval.rs

@@ -741,6 +741,25 @@ pub fn simple_eval(
                 let output = input.to_dtype(dtype)?;
                 values.insert(node.output[0].clone(), output);
             }
+            // https://github.com/onnx/onnx/blob/main/docs/Operators.md#CumSum
+            "CumSum" => {
+                let exclusive = get_attr_opt::<i64>(node, "exclusive")?
+                    .copied()
+                    .unwrap_or(0);
+                let reverse = get_attr_opt::<i64>(node, "reverse")?.copied().unwrap_or(0);
+                if exclusive != 0 {
+                    bail!("only exclusive == 0 is supported in CumSum")
+                }
+                if reverse != 0 {
+                    bail!("only reverse == 0 is supported in CumSum")
+                }
+                let input = get(&node.input[0])?;
+                let axis = get(&node.input[1])?
+                    .to_dtype(DType::U32)?
+                    .to_vec0::<u32>()?;
+                let output = input.cumsum(axis as usize)?;
+                values.insert(node.output[0].clone(), output);
+            }
             op_type => bail!("unsupported op_type {op_type} for op {node:?}"),
         }
     }

candle-onnx/tests/ops.rs

@@ -0,0 +1,746 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{Device, Result, Tensor};
+use candle_onnx::onnx::{GraphProto, ModelProto, NodeProto, ValueInfoProto};
+use std::collections::HashMap;
+
+const INPUT_X: &str = "x";
+const INPUT_Y: &str = "y";
+const OUTPUT_Z: &str = "z";
+
+fn create_model_proto_with_graph(graph: Option<GraphProto>) -> ModelProto {
+    ModelProto {
+        metadata_props: vec![],
+        training_info: vec![],
+        functions: vec![],
+        ir_version: 0,
+        opset_import: vec![],
+        producer_name: "".to_string(),
+        producer_version: "".to_string(),
+        domain: "".to_string(),
+        model_version: 0,
+        doc_string: "".to_string(),
+        graph,
+    }
+}
+
+#[test]
+fn test_evaluation_fails_without_defined_graph() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(None);
+
+    let inputs: HashMap<String, Tensor> = HashMap::new();
+
+    match candle_onnx::simple_eval(&manual_graph, inputs) {
+        Err(err) => assert_eq!(err.to_string(), "no graph defined in proto"),
+        Ok(_) => panic!("Expected an error due to undefined graph"),
+    }
+
+    Ok(())
+}
+
+// "Add"
+#[test]
+fn test_add_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Add".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+    inputs.insert(INPUT_Y.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+
+    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 first = z
+        .to_vec1::<f64>()?
+        .to_vec()
+        .get(0)
+        .expect("Failed to get first element")
+        .clone();
+    assert_eq!(first, 4.0f64);
+
+    Ok(())
+}
+
+// "Sub"
+#[test]
+fn test_sub_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Sub".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+    inputs.insert(INPUT_Y.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+
+    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 first = z
+        .to_vec1::<f64>()?
+        .to_vec()
+        .get(0)
+        .expect("Failed to get first element")
+        .clone();
+    assert_eq!(first, 0.0f64);
+
+    Ok(())
+}
+
+// "Mul"
+#[test]
+fn test_mul_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Mul".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+    inputs.insert(INPUT_Y.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+
+    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 first = z
+        .to_vec1::<f64>()?
+        .to_vec()
+        .get(0)
+        .expect("Failed to get first element")
+        .clone();
+    assert_eq!(first, 4.0f64);
+
+    Ok(())
+}
+
+// "Div"
+#[test]
+fn test_div_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Div".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+    inputs.insert(INPUT_Y.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+
+    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 first = z
+        .to_vec1::<f64>()?
+        .to_vec()
+        .get(0)
+        .expect("Failed to get first element")
+        .clone();
+
+    assert_eq!(first, 1.0f64);
+
+    Ok(())
+}
+
+// "Equal"
+#[test]
+fn test_equal_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Equal".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+    inputs.insert(INPUT_Y.to_string(), Tensor::new(&[2.], &Device::Cpu)?);
+
+    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 first = z.to_dtype(candle::DType::U8)?.to_vec1::<u8>()?.to_vec()[0];
+    assert_eq!(first, 1);
+
+    Ok(())
+}
+
+// "Not"
+#[test]
+fn test_not_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Not".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.to_string()],
+            output: vec![OUTPUT_Z.to_string()],
+            name: "".to_string(),
+            doc_string: "".to_string(),
+        }],
+        name: "".to_string(),
+        initializer: vec![],
+        input: vec![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), Tensor::new(&[0.], &Device::Cpu)?);
+
+    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 first = z.to_dtype(candle::DType::U8)?.to_vec1::<u8>()?.to_vec()[0];
+    assert_eq!(first, 1);
+
+    Ok(())
+}
+
+// "MatMul"
+#[test]
+fn test_matmul_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "MatMul".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![],
+        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 mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(
+        INPUT_X.to_string(),
+        Tensor::from_vec(
+            //
+            vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+            &[2, 2],
+            &Device::Cpu,
+        )?,
+    );
+    inputs.insert(
+        INPUT_Y.to_string(),
+        Tensor::from_vec(
+            //
+            vec![5.0f32, 6.0f32, 7.0f32, 8.0f32],
+            &[2, 2],
+            &Device::Cpu,
+        )?,
+    );
+
+    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![19.0, 22.0], vec![43.0, 50.0]]);
+
+    Ok(())
+}
+
+// "Reshape"
+#[test]
+fn test_reshape_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Reshape".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::from_vec(
+        //
+        vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+    let y = Tensor::from_vec(
+        //
+        vec![4i64],
+        &[1],
+        &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_vec1::<f32>()?;
+
+    assert_eq!(results, vec![1.0, 2.0, 3.0, 4.0]);
+
+    Ok(())
+}
+
+// "LogSoftmax"
+#[test]
+fn test_logsoftmax_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "LogSoftmax".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.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::from_vec(
+        //
+        vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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![0.26894143, 0.7310586], vec![0.26894143, 0.7310586]]
+    );
+
+    Ok(())
+}
+
+// "Softmax"
+#[test]
+fn test_softmax_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Softmax".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.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::from_vec(
+        //
+        vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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![0.26894143, 0.7310586], vec![0.26894143, 0.7310586]]
+    );
+
+    Ok(())
+}
+
+// "Transpose"
+#[test]
+fn test_transpose_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Transpose".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.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::from_vec(
+        //
+        vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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, 3.0], vec![2.0, 4.0]]);
+
+    Ok(())
+}
+
+// "Dropout"
+#[test]
+fn test_dropout_operation() -> Result<()> {
+    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
+        node: vec![NodeProto {
+            op_type: "Dropout".to_string(),
+            domain: "".to_string(),
+            attribute: vec![],
+            input: vec![INPUT_X.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::from_vec(
+        //
+        vec![1.0f32, 2.0f32, 3.0f32, 4.0f32],
+        &[2, 2],
+        &Device::Cpu,
+    )?;
+
+    let mut inputs: HashMap<String, Tensor> = HashMap::new();
+    inputs.insert(INPUT_X.to_string(), x);
+
+    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, 2.0], vec![3.0, 4.0]]);
+
+    Ok(())
+}
+
+// Below are ops that are implemented but not tested yet
+
+// "MaxPool"
+// #[test]
+
+// "AveragePool"
+// #[test]
+
+// "BatchNormalization"
+// #[test]
+
+// "Squeeze"
+// #[test]
+
+// "ConstantOfShape"
+// #[test]
+
+// "Unsqueeze"
+// #[test]
+
+// "Clip"
+// #[test]
+
+// "Gather"
+// #[test]
+
+// "Shape"
+// #[test]
+
+// "Conv"
+// #[test]
+
+// "Concat"
+// #[test]
+
+// "Abs"
+// #[test]
+
+// "Cos"
+// #[test]
+
+// "Sin"
+// #[test]
+
+// "Neg"
+// #[test]
+
+// "Erf"
+// #[test]
+
+// "Tanh"
+// #[test]
+
+// "Sigmoid"
+// #[test]
+
+// "Gelu"
+// #[test]
+
+// "Relu"
+// #[test]
+
+// "Constant"
+// #[test]
+
+// "Cast"
+// #[test]

candle-pyo3/Cargo.toml

@@ -15,9 +15,9 @@ crate-type = ["cdylib"]
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
-candle-onnx = {path= "../candle-onnx", version = "0.3.0", optional = true}
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
+candle-onnx = {path= "../candle-onnx", version = "0.3.1", optional = true}
 half = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
 pyo3 = { version = "0.20.0", features = ["extension-module", "abi3-py38"] }

candle-pyo3/src/lib.rs

@@ -17,7 +17,7 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use ::candle::{quantized::QTensor, DType, Device, Tensor, WithDType};
+use ::candle::{quantized::QTensor, DType, Device, Module, Tensor, WithDType};
 
 mod utils;
 use utils::wrap_err;

candle-transformers/Cargo.toml

@@ -12,15 +12,16 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.1", optional = true }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
 rand = { workspace = true }
 rayon = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
+serde_plain = { workspace = true }
 tracing = { workspace = true }
 wav = { workspace = true }
 
@@ -30,3 +31,4 @@ accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate"]
 cuda = ["candle/cuda", "candle-nn/cuda"]
 flash-attn = ["cuda", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl"]
+metal = ["candle/metal", "candle-nn/metal"]

candle-transformers/src/models/distilbert.rs

@@ -0,0 +1,342 @@
+use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
+use candle::{DType, Device, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use serde::Deserialize;
+
+pub const DTYPE: DType = DType::F32;
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)]
+#[serde(rename_all = "lowercase")]
+enum HiddenAct {
+    Gelu,
+    Relu,
+}
+
+struct HiddenActLayer {
+    act: HiddenAct,
+    span: tracing::Span,
+}
+
+impl HiddenActLayer {
+    fn new(act: HiddenAct) -> Self {
+        let span = tracing::span!(tracing::Level::TRACE, "hidden-act");
+        Self { act, span }
+    }
+}
+
+impl Module for HiddenActLayer {
+    fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
+        let _enter = self.span.enter();
+        match self.act {
+            // https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/activations.py#L213
+            HiddenAct::Gelu => xs.gelu(),
+            HiddenAct::Relu => xs.relu(),
+        }
+    }
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize, Default)]
+#[serde(rename_all = "lowercase")]
+enum PositionEmbeddingType {
+    #[default]
+    Absolute,
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Config {
+    vocab_size: usize,
+    dim: usize,
+    n_layers: usize,
+    n_heads: usize,
+    hidden_dim: usize,
+    activation: HiddenAct,
+    max_position_embeddings: usize,
+    initializer_range: f64,
+    pad_token_id: usize,
+    #[serde(default)]
+    position_embedding_type: PositionEmbeddingType,
+    #[serde(default)]
+    use_cache: bool,
+    model_type: Option<String>,
+}
+
+impl Default for Config {
+    fn default() -> Self {
+        Self {
+            vocab_size: 30522,
+            dim: 768,
+            n_layers: 12,
+            n_heads: 12,
+            hidden_dim: 3072,
+            activation: HiddenAct::Gelu,
+            max_position_embeddings: 512,
+            initializer_range: 0.02,
+            pad_token_id: 0,
+            position_embedding_type: PositionEmbeddingType::Absolute,
+            use_cache: true,
+            model_type: Some("distilbert".to_string()),
+        }
+    }
+}
+
+struct Embeddings {
+    word_embeddings: Embedding,
+    position_embeddings: Embedding,
+    layer_norm: LayerNorm,
+    span: tracing::Span,
+}
+
+impl Embeddings {
+    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let word_embeddings =
+            candle_nn::embedding(config.vocab_size, config.dim, vb.pp("word_embeddings"))?;
+        let position_embeddings = candle_nn::embedding(
+            config.max_position_embeddings,
+            config.dim,
+            vb.pp("position_embeddings"),
+        )?;
+        let layer_norm = layer_norm(config.dim, 1e-12, vb.pp("LayerNorm"))?;
+        Ok(Self {
+            word_embeddings,
+            position_embeddings,
+            layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "embeddings"),
+        })
+    }
+
+    fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let (_bsize, seq_len) = input_ids.dims2()?;
+        let input_embeddings = self.word_embeddings.forward(input_ids)?;
+        let position_ids = (0..seq_len as u32).collect::<Vec<_>>();
+        let position_ids = Tensor::new(&position_ids[..], input_ids.device())?;
+        let embeddings =
+            input_embeddings.broadcast_add(&self.position_embeddings.forward(&position_ids)?)?;
+
+        let embeddings = self.layer_norm.forward(&embeddings)?;
+        Ok(embeddings)
+    }
+}
+
+struct MultiHeadSelfAttention {
+    q_lin: Linear,
+    k_lin: Linear,
+    v_lin: Linear,
+    out_lin: Linear,
+    n_heads: usize,
+    attention_head_size: usize,
+    span: tracing::Span,
+}
+
+impl MultiHeadSelfAttention {
+    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let attention_head_size = config.dim / config.n_heads;
+        let all_head_size = config.n_heads * attention_head_size;
+        let dim = config.dim;
+        let q_lin = linear(dim, all_head_size, vb.pp("q_lin"))?;
+        let v_lin = linear(dim, all_head_size, vb.pp("v_lin"))?;
+        let k_lin = linear(dim, all_head_size, vb.pp("k_lin"))?;
+        let out_lin = linear(all_head_size, dim, vb.pp("out_lin"))?;
+        Ok(Self {
+            q_lin,
+            k_lin,
+            v_lin,
+            out_lin,
+            n_heads: config.n_heads,
+            attention_head_size,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+}
+
+impl MultiHeadSelfAttention {
+    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let (bs, q_length, _dim) = hidden_states.dims3()?;
+
+        let dim_per_head = self.attention_head_size;
+        let q = self.q_lin.forward(hidden_states)?;
+        let k = self.k_lin.forward(hidden_states)?;
+        let v = self.v_lin.forward(hidden_states)?;
+
+        let q = q
+            .reshape((bs, q_length, self.n_heads, dim_per_head))?
+            .transpose(1, 2)?;
+        let k = k
+            .reshape((bs, q_length, self.n_heads, dim_per_head))?
+            .transpose(1, 2)?;
+        let v = v
+            .reshape((bs, q_length, self.n_heads, dim_per_head))?
+            .transpose(1, 2)?;
+
+        let q: Tensor = (q / (dim_per_head as f64).sqrt())?;
+        let scores = q.matmul(&k.transpose(2, 3)?.contiguous()?)?;
+        let mask = attention_mask.broadcast_as(scores.shape())?;
+
+        let scores = masked_fill(&scores.to_dtype(DType::F32)?, &mask, f32::NEG_INFINITY)?;
+        let weights = candle_nn::ops::softmax(&scores, candle::D::Minus1)?;
+
+        let context = weights.matmul(&v.contiguous()?)?;
+        let context = context
+            .transpose(1, 2)?
+            .reshape((bs, q_length, self.n_heads * dim_per_head))?
+            .contiguous()?;
+        let context = self.out_lin.forward(&context)?;
+
+        Ok(context)
+    }
+}
+
+#[allow(clippy::upper_case_acronyms)]
+struct FFN {
+    lin1: Linear,
+    lin2: Linear,
+    activation: HiddenActLayer,
+    span: tracing::Span,
+}
+
+impl FFN {
+    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let lin1 = linear(config.dim, config.hidden_dim, vb.pp("lin1"))?;
+        let lin2 = linear(config.hidden_dim, config.dim, vb.pp("lin2"))?;
+        Ok(Self {
+            lin1,
+            lin2,
+            activation: HiddenActLayer::new(config.activation),
+            span: tracing::span!(tracing::Level::TRACE, "ffn"),
+        })
+    }
+}
+
+impl Module for FFN {
+    fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        hidden_states
+            .apply(&self.lin1)?
+            .apply(&self.activation)?
+            .apply(&self.lin2)
+    }
+}
+
+struct TransformerBlock {
+    attention: MultiHeadSelfAttention,
+    sa_layer_norm: LayerNorm,
+    ffn: FFN,
+    output_layer_norm: LayerNorm,
+    span: tracing::Span,
+}
+
+impl TransformerBlock {
+    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let attention = MultiHeadSelfAttention::load(vb.pp("attention"), config)?;
+        let sa_layer_norm = layer_norm(config.dim, 1e-12, vb.pp("sa_layer_norm"))?;
+        let ffn = FFN::load(vb.pp("ffn"), config)?;
+        let output_layer_norm = layer_norm(config.dim, 1e-12, vb.pp("output_layer_norm"))?;
+        Ok(Self {
+            attention,
+            sa_layer_norm,
+            ffn,
+            output_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+}
+
+impl TransformerBlock {
+    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let sa_output = self.attention.forward(hidden_states, attention_mask)?;
+        // TODO: Support cross-attention?
+        // https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L523
+        // TODO: Support something similar to `apply_chunking_to_forward`?
+        let sa_output = sa_output.broadcast_add(hidden_states)?;
+        let sa_output = self.sa_layer_norm.forward(&sa_output)?;
+
+        let ffn_output = self.ffn.forward(&sa_output)?;
+        let ffn_output = (&ffn_output + sa_output)?;
+        let output = self.output_layer_norm.forward(&ffn_output)?;
+        Ok(output)
+    }
+}
+
+// https://github.com/huggingface/transformers/blob/6eedfa6dd15dc1e22a55ae036f681914e5a0d9a1/src/transformers/models/bert/modeling_bert.py#L556
+struct Transformer {
+    layers: Vec<TransformerBlock>,
+    span: tracing::Span,
+}
+
+impl Transformer {
+    fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let layers = (0..config.n_layers)
+            .map(|index| TransformerBlock::load(vb.pp(&format!("layer.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+        let span = tracing::span!(tracing::Level::TRACE, "encoder");
+        Ok(Transformer { layers, span })
+    }
+}
+
+impl Transformer {
+    fn forward(&self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let mut hidden_states = hidden_states.clone();
+        // Use a loop rather than a fold as it's easier to modify when adding debug/...
+        for layer in self.layers.iter() {
+            hidden_states = layer.forward(&hidden_states, attention_mask)?;
+        }
+        Ok(hidden_states)
+    }
+}
+
+pub struct DistilBertModel {
+    embeddings: Embeddings,
+    transformer: Transformer,
+    pub device: Device,
+    span: tracing::Span,
+}
+
+impl DistilBertModel {
+    pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
+        let (embeddings, transformer) = match (
+            Embeddings::load(vb.pp("embeddings"), config),
+            Transformer::load(vb.pp("transformer"), config),
+        ) {
+            (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
+            (Err(err), _) | (_, Err(err)) => {
+                if let Some(model_type) = &config.model_type {
+                    if let (Ok(embeddings), Ok(encoder)) = (
+                        Embeddings::load(vb.pp(&format!("{model_type}.embeddings")), config),
+                        Transformer::load(vb.pp(&format!("{model_type}.transformer")), config),
+                    ) {
+                        (embeddings, encoder)
+                    } else {
+                        return Err(err);
+                    }
+                } else {
+                    return Err(err);
+                }
+            }
+        };
+        Ok(Self {
+            embeddings,
+            transformer,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, input_ids: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let embedding_output = self.embeddings.forward(input_ids)?;
+        let sequence_output = self
+            .transformer
+            .forward(&embedding_output, attention_mask)?;
+        Ok(sequence_output)
+    }
+}

candle-transformers/src/models/llama2_c.rs

@@ -156,7 +156,6 @@ impl CausalSelfAttention {
         let x = x.reshape((b_sz, seq_len, h, n_embd / 2, 2))?;
         let x0 = x.narrow(D::Minus1, 0, 1)?;
         let x1 = x.narrow(D::Minus1, 1, 1)?;
-        todo!("X {x1}");
         let dst0 = (x0.broadcast_mul(&cos)? - x1.broadcast_mul(&sin)?)?;
         let dst1 = (x0.broadcast_mul(&sin)? + x1.broadcast_mul(&cos)?)?;
         let rope = Tensor::cat(&[&dst0, &dst1], D::Minus1)?.reshape((b_sz, seq_len, h, n_embd))?;
@@ -174,7 +173,6 @@ impl CausalSelfAttention {
         let mut v = v.reshape((b_sz, seq_len, self.n_key_value_head, self.head_dim))?;
 
         let q = self.apply_rotary_emb(&q, index_pos)?;
-        todo!("X {q}");
         let mut k = self.apply_rotary_emb(&k, index_pos)?;
 
         if self.cache.use_kv_cache {
@@ -297,7 +295,6 @@ impl Block {
         let residual = x;
         let x = self.rms_1.forward(x)?;
         let x = (self.attn.forward(&x, index_pos, block_idx)? + residual)?;
-        todo!("---X {}", x);
         let residual = &x;
         let x = (self.mlp.forward(&self.rms_2.forward(&x)?)? + residual)?;
         Ok(x)
@@ -330,7 +327,6 @@ impl Llama {
     pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
         let (_b_sz, _seq_len) = x.dims2()?;
         let mut x = self.wte.forward(x)?;
-        //println!("Embeddings {}", self.wte.embeddings());
         for (block_idx, block) in self.blocks.iter().enumerate() {
             x = block.forward(&x, index_pos, block_idx)?;
         }

candle-transformers/src/models/mixformer.rs

@@ -142,10 +142,10 @@ impl RotaryEmbedding {
             .to_dtype(DType::F32)?
             .reshape((max_seq_len, 1))?;
         let freqs = t.matmul(&inv_freq)?;
-        Ok(Self {
-            sin: freqs.sin()?,
-            cos: freqs.cos()?,
-        })
+        let sin = freqs.sin()?;
+        let cos = freqs.cos()?;
+        // todo!("{}", sin);
+        Ok(Self { sin, cos })
     }
 
     fn apply_rotary_emb_qkv(
@@ -273,6 +273,10 @@ impl MHA {
     }
 
     fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
+        // let view = xs.to_string();
+        // if view.contains("NaN") {
+        //     panic!("NaN");
+        // }
         let _enter = self.span.enter();
         let (b_size, seq_len, _n_embd) = xs.dims3()?;
         let qkv = self
@@ -408,3 +412,38 @@ impl MixFormerSequentialForCausalLM {
         self.blocks.iter_mut().for_each(|b| b.clear_kv_cache())
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test]
+    fn test_rotary() {
+        let dev = Device::new_metal(0).unwrap();
+        for i in 0..10000 {
+            let dim = 8;
+            let max_seq_len = 12;
+            let inv_freq: Vec<_> = (0..dim)
+                .step_by(2)
+                .map(|i| 1f32 / 10000f32.powf(i as f32 / dim as f32))
+                .collect();
+            let inv_freq_len = inv_freq.len();
+            let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), &dev).unwrap();
+            let t = Tensor::arange(0u32, max_seq_len as u32, &dev)
+                .unwrap()
+                .to_dtype(DType::F32)
+                .unwrap()
+                .reshape((max_seq_len, 1))
+                .unwrap();
+            let x: f32 = t.i((1, 0)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 1.0);
+            let x: f32 = inv_freq.i((0, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.1);
+            let freqs = t.matmul(&inv_freq).unwrap();
+            let x: f32 = freqs.i((1, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.1);
+            let sin = freqs.sin().unwrap().contiguous().unwrap();
+            let x: f32 = sin.i((1, 1)).unwrap().to_scalar().unwrap();
+            assert_eq!(x, 0.099833414);
+        }
+    }
+}

candle-transformers/src/models/mod.rs

@@ -4,6 +4,7 @@ pub mod blip;
 pub mod blip_text;
 pub mod convmixer;
 pub mod dinov2;
+pub mod distilbert;
 pub mod efficientnet;
 pub mod falcon;
 pub mod jina_bert;
@@ -29,8 +30,10 @@ pub mod segment_anything;
 pub mod stable_diffusion;
 pub mod stable_lm;
 pub mod t5;
+pub mod trocr;
 pub mod vgg;
 pub mod vit;
 pub mod whisper;
 pub mod with_tracing;
 pub mod wuerstchen;
+pub mod yi;

candle-transformers/src/models/quantized_t5.rs

@@ -1,6 +1,7 @@
 // T5 Text Model, quantized version
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
 
+use crate::models::t5::{deserialize_feed_forward_proj_activation, ActivationWithOptionalGating};
 use crate::models::with_tracing::QMatMul;
 use crate::quantized_nn::Embedding;
 pub use crate::quantized_var_builder::VarBuilder;
@@ -54,8 +55,8 @@ pub struct Config {
     dropout_rate: f64,
     layer_norm_epsilon: f64,
     initializer_factor: f64,
-    #[serde(default)]
-    feed_forward_proj: Activation,
+    #[serde(default, deserialize_with = "deserialize_feed_forward_proj_activation")]
+    pub feed_forward_proj: ActivationWithOptionalGating,
     #[serde(default = "default_tie_word_embeddings")]
     tie_word_embeddings: bool,
     #[serde(default = "default_is_decoder")]
@@ -83,7 +84,10 @@ impl Default for Config {
             dropout_rate: 0.1,
             layer_norm_epsilon: 1e-6,
             initializer_factor: 1.0,
-            feed_forward_proj: Activation::Relu,
+            feed_forward_proj: ActivationWithOptionalGating {
+                gated: false,
+                activation: Activation::Relu,
+            },
             tie_word_embeddings: true,
             is_decoder: false,
             is_encoder_decoder: true,
@@ -176,7 +180,7 @@ impl T5DenseGatedActDense {
             wi_0,
             wi_1,
             wo,
-            act: Activation::NewGelu,
+            act: cfg.feed_forward_proj.activation,
             span: tracing::span!(tracing::Level::TRACE, "dense-gated-act-dense"),
         })
     }
@@ -205,7 +209,7 @@ impl T5LayerFF {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let layer_norm =
             T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
-        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj == Activation::NewGelu {
+        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj.gated {
             (
                 None,
                 Some(T5DenseGatedActDense::load(vb.pp("DenseReluDense"), cfg)?),

candle-transformers/src/models/stable_diffusion/utils.rs

@@ -1,12 +1,15 @@
 use candle::{Device, Result, Tensor};
 
 pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
-    if steps < 1 {
-        candle::bail!("cannot use linspace with steps {steps} <= 1")
+    if steps == 0 {
+        Tensor::from_vec(Vec::<f64>::new(), steps, &Device::Cpu)
+    } else if steps == 1 {
+        Tensor::from_vec(vec![start], steps, &Device::Cpu)
+    } else {
+        let delta = (stop - start) / (steps - 1) as f64;
+        let vs = (0..steps)
+            .map(|step| start + step as f64 * delta)
+            .collect::<Vec<_>>();
+        Tensor::from_vec(vs, steps, &Device::Cpu)
     }
-    let delta = (stop - start) / (steps - 1) as f64;
-    let vs = (0..steps)
-        .map(|step| start + step as f64 * delta)
-        .collect::<Vec<_>>();
-    Tensor::from_vec(vs, steps, &Device::Cpu)
 }

candle-transformers/src/models/t5.rs

@@ -37,6 +37,37 @@ fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor>
     Ok(m)
 }
 
+#[derive(Debug, Deserialize, Default, Clone, PartialEq)]
+pub struct ActivationWithOptionalGating {
+    pub gated: bool,
+    pub activation: candle_nn::Activation,
+}
+
+pub fn deserialize_feed_forward_proj_activation<'de, D>(
+    deserializer: D,
+) -> std::result::Result<ActivationWithOptionalGating, D::Error>
+where
+    D: serde::de::Deserializer<'de>,
+{
+    match String::deserialize(deserializer)?.as_str() {
+        "gated-gelu" => Ok(ActivationWithOptionalGating {
+            gated: true,
+            activation: candle_nn::Activation::NewGelu,
+        }),
+        "gated-silu" => Ok(ActivationWithOptionalGating {
+            gated: true,
+            activation: candle_nn::Activation::Silu,
+        }),
+        buf => {
+            let activation = serde_plain::from_str(buf).map_err(serde::de::Error::custom)?;
+            Ok(ActivationWithOptionalGating {
+                gated: false,
+                activation,
+            })
+        }
+    }
+}
+
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Config {
     vocab_size: usize,
@@ -52,8 +83,8 @@ pub struct Config {
     dropout_rate: f64,
     layer_norm_epsilon: f64,
     initializer_factor: f64,
-    #[serde(default)]
-    feed_forward_proj: Activation,
+    #[serde(default, deserialize_with = "deserialize_feed_forward_proj_activation")]
+    feed_forward_proj: ActivationWithOptionalGating,
     #[serde(default = "default_tie_word_embeddings")]
     tie_word_embeddings: bool,
     #[serde(default = "default_is_decoder")]
@@ -81,7 +112,10 @@ impl Default for Config {
             dropout_rate: 0.1,
             layer_norm_epsilon: 1e-6,
             initializer_factor: 1.0,
-            feed_forward_proj: Activation::Relu,
+            feed_forward_proj: ActivationWithOptionalGating {
+                gated: false,
+                activation: Activation::Relu,
+            },
             tie_word_embeddings: true,
             is_decoder: false,
             is_encoder_decoder: true,
@@ -102,7 +136,10 @@ impl Config {
             d_model: 768,
             dropout_rate: 0.1,
             eos_token_id: 1,
-            feed_forward_proj: Activation::Relu,
+            feed_forward_proj: ActivationWithOptionalGating {
+                gated: false,
+                activation: Activation::Relu,
+            },
             tie_word_embeddings: true,
             initializer_factor: 1.0,
             is_decoder: false,
@@ -202,7 +239,7 @@ impl T5DenseGatedActDense {
             wi_0,
             wi_1,
             wo,
-            act: Activation::NewGelu,
+            act: cfg.feed_forward_proj.activation,
             span: tracing::span!(tracing::Level::TRACE, "dense-gated-act-dense"),
         })
     }
@@ -231,7 +268,7 @@ impl T5LayerFF {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let layer_norm =
             T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
-        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj == Activation::NewGelu {
+        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj.gated {
             (
                 None,
                 Some(T5DenseGatedActDense::load(vb.pp("DenseReluDense"), cfg)?),
@@ -425,7 +462,7 @@ impl T5Attention {
                                             self.relative_attention_max_distance as f32
                                                 / max_exact as f32,
                                         ) * (num_buckets - max_exact) as f32;
-                                        max_exact + b as u32
+                                        u32::min(max_exact + b as u32, num_buckets - 1)
                                     }
                                 })
                                 .collect::<Vec<u32>>()

candle-transformers/src/models/trocr.rs

@@ -0,0 +1,454 @@
+use crate::models::vit::{Config, Embeddings, Encoder};
+use candle::{Result, Tensor};
+use candle_nn::{
+    embedding, layer_norm, linear_no_bias, Embedding, LayerNorm, Linear, Module, VarBuilder,
+};
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct TrOCRConfig {
+    pub vocab_size: usize,
+    pub d_model: usize,
+    pub hidden_size: usize,
+    pub decoder_layers: usize,
+    pub decoder_attention_heads: usize,
+    pub decoder_ffn_dim: usize,
+    pub activation_function: candle_nn::Activation,
+    pub max_position_embeddings: usize,
+    pub dropout: f64,
+    pub attention_dropout: f64,
+    pub activation_dropout: f64,
+    pub decoder_start_token_id: u32,
+    pub init_std: f64,
+    pub decoder_layerdrop: f64,
+    pub use_cache: bool,
+    pub scale_embedding: bool,
+    pub use_learned_position_embeddings: bool,
+    pub layernorm_embedding: bool,
+    pub pad_token_id: usize,
+    pub bos_token_id: usize,
+    pub eos_token_id: u32,
+    pub num_attention_heads: usize,
+    pub decoder_vocab_size: Option<usize>,
+}
+
+impl Default for TrOCRConfig {
+    fn default() -> Self {
+        Self {
+            vocab_size: 50265,
+            d_model: 1024,
+            hidden_size: 768,
+            decoder_layers: 12,
+            decoder_attention_heads: 16,
+            decoder_ffn_dim: 4096,
+            activation_function: candle_nn::Activation::Gelu,
+            max_position_embeddings: 512,
+            dropout: 0.1,
+            attention_dropout: 0.0,
+            activation_dropout: 0.0,
+            decoder_start_token_id: 2,
+            init_std: 0.02,
+            decoder_layerdrop: 0.0,
+            use_cache: true,
+            scale_embedding: false,
+            use_learned_position_embeddings: true,
+            layernorm_embedding: true,
+            pad_token_id: 1,
+            bos_token_id: 0,
+            eos_token_id: 2,
+            num_attention_heads: 12,
+            decoder_vocab_size: Some(50265),
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRLearnedPositionalEmbedding {
+    offset: usize,
+    weights: Embedding,
+}
+
+impl TrOCRLearnedPositionalEmbedding {
+    fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
+        let offset: usize = 2;
+        let num_embeddings = cfg.max_position_embeddings;
+        let embedding_dim = cfg.d_model;
+        let weights = embedding(num_embeddings + offset, embedding_dim, vb)?;
+
+        Ok(Self { offset, weights })
+    }
+
+    fn forward(&mut self, input_ids: &Tensor, past_key_values_length: u32) -> Result<Tensor> {
+        let (b_sz, seq_len) = input_ids.dims2()?;
+
+        let mut positions = Tensor::arange(
+            past_key_values_length,
+            seq_len as u32 + past_key_values_length,
+            input_ids.device(),
+        )?
+        .expand((b_sz, seq_len))?;
+
+        positions =
+            positions.broadcast_add(&Tensor::new(self.offset as u32, input_ids.device())?)?;
+        self.weights.forward(&positions)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRAttention {
+    head_dim: usize,
+    num_heads: usize,
+    is_decoder: bool,
+    scaling: f64,
+    k_proj: Linear,
+    v_proj: Linear,
+    q_proj: Linear,
+    out_proj: Linear,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl TrOCRAttention {
+    fn load(
+        vb: VarBuilder,
+        cfg: &TrOCRConfig,
+        kdim: Option<usize>,
+        vdim: Option<usize>,
+    ) -> Result<Self> {
+        let embed_dim = cfg.d_model;
+        let num_heads = cfg.decoder_attention_heads;
+        let head_dim = embed_dim / num_heads;
+        let kdim = kdim.unwrap_or(embed_dim);
+        let vdim = vdim.unwrap_or(embed_dim);
+
+        let k_proj = linear_no_bias(kdim, embed_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_no_bias(vdim, embed_dim, vb.pp("v_proj"))?;
+        let q_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("q_proj"))?;
+
+        let out_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("out_proj"))?;
+        Ok(Self {
+            head_dim,
+            num_heads,
+            is_decoder: true,
+            scaling: 1. / (head_dim as f64).sqrt(),
+            k_proj,
+            v_proj,
+            q_proj,
+            out_proj,
+            kv_cache: None,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+
+    fn _shape(&self, tensor: &Tensor, bsz: usize) -> Result<Tensor> {
+        tensor
+            .reshape((bsz, (), self.num_heads, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        kv_states: Option<&Tensor>,
+        attn_mask: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let (b_sz, tgt_len, _) = xs.dims3()?;
+        let query_states = (xs.apply(&self.q_proj)? * self.scaling)?;
+        let (key_states, value_states) = match kv_states {
+            None => {
+                let key_states = self._shape(&xs.apply(&self.k_proj)?, b_sz)?;
+                let value_states = self._shape(&xs.apply(&self.v_proj)?, b_sz)?;
+                if self.is_decoder {
+                    let kv_states = match &self.kv_cache {
+                        None => (key_states, value_states),
+                        Some((p_key_states, p_value_states)) => {
+                            let key_states = Tensor::cat(&[p_key_states, &key_states], 2)?;
+                            let value_states = Tensor::cat(&[p_value_states, &value_states], 2)?;
+                            (key_states, value_states)
+                        }
+                    };
+                    self.kv_cache = Some(kv_states.clone());
+                    kv_states
+                } else {
+                    (key_states, value_states)
+                }
+            }
+            Some(kv_states) => {
+                let key_states = self._shape(&kv_states.apply(&self.k_proj)?, b_sz)?;
+                let value_states = self._shape(&kv_states.apply(&self.v_proj)?, b_sz)?;
+                (key_states, value_states)
+            }
+        };
+        let proj_shape = (b_sz * self.num_heads, (), self.head_dim);
+        let query_states = self._shape(&query_states, b_sz)?.reshape(proj_shape)?;
+        let key_states = key_states.reshape(proj_shape)?;
+        let value_states = value_states.reshape(proj_shape)?;
+        let attn_weights = query_states.matmul(&key_states.transpose(1, 2)?)?;
+        let attn_weights = match attn_mask {
+            None => attn_weights,
+            Some(attn_mask) => attn_weights.broadcast_add(attn_mask)?,
+        };
+        let attn_probs = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+        let attn_output = attn_probs.matmul(&value_states)?;
+        attn_output
+            .reshape((b_sz, self.num_heads, tgt_len, self.head_dim))?
+            .transpose(1, 2)?
+            .reshape((b_sz, tgt_len, self.head_dim * self.num_heads))?
+            .apply(&self.out_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRDecoderLayer {
+    self_attn: TrOCRAttention,
+    activation_fn: candle_nn::Activation,
+    self_attn_layer_norm: LayerNorm,
+    encoder_attn: TrOCRAttention,
+    encoder_attn_layer_norm: LayerNorm,
+    fc1: Linear,
+    fc2: Linear,
+    final_layer_norm: LayerNorm,
+}
+
+impl TrOCRDecoderLayer {
+    fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
+        let embed_dim = cfg.d_model;
+        let self_attn = TrOCRAttention::load(vb.pp("self_attn"), cfg, None, None)?;
+        let self_attn_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("self_attn_layer_norm"))?;
+        let encoder_attn = TrOCRAttention::load(
+            vb.pp("encoder_attn"),
+            cfg,
+            Some(cfg.hidden_size),
+            Some(cfg.hidden_size),
+        )?;
+        let encoder_attn_layer_norm =
+            layer_norm(embed_dim, 1e-5, vb.pp("encoder_attn_layer_norm"))?;
+        let fc1 = linear_no_bias(embed_dim, cfg.decoder_ffn_dim, vb.pp("fc1"))?;
+        let fc2 = linear_no_bias(cfg.decoder_ffn_dim, embed_dim, vb.pp("fc2"))?;
+        let final_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("final_layer_norm"))?;
+        let activation_fn = candle_nn::Activation::Gelu;
+
+        Ok(Self {
+            self_attn,
+            activation_fn,
+            self_attn_layer_norm,
+            encoder_attn,
+            encoder_attn_layer_norm,
+            fc1,
+            fc2,
+            final_layer_norm,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.self_attn.reset_kv_cache();
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: &Tensor,
+        encoder_hidden_states: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let residual = xs.clone();
+        let xs = self.self_attn.forward(xs, None, Some(attention_mask))?;
+        let xs = (xs + residual)?;
+        let mut xs = self.self_attn_layer_norm.forward(&xs)?;
+
+        if let Some(encoder_hidden_states) = &encoder_hidden_states {
+            let residual = xs.clone();
+            let encoder_attention_mask = attention_mask.clone(); // TODO
+            xs = self.encoder_attn.forward(
+                &xs,
+                Some(encoder_hidden_states),
+                Some(&encoder_attention_mask),
+            )?;
+            xs = (xs + residual)?;
+            xs = self.encoder_attn_layer_norm.forward(&xs)?
+        }
+
+        let residual = xs.clone();
+        let xs = self.fc1.forward(&xs)?;
+        let xs = self.activation_fn.forward(&xs)?;
+        let xs = self.fc2.forward(&xs)?;
+        let xs = (xs + residual)?;
+        let xs = self.final_layer_norm.forward(&xs)?;
+
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRDecoder {
+    layers: Vec<TrOCRDecoderLayer>,
+    embed_scale: Option<f64>,
+    embed_tokens: Embedding,
+    embed_positions: TrOCRLearnedPositionalEmbedding,
+}
+
+impl TrOCRDecoder {
+    fn new(cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let vb = vb.pp("decoder.model.decoder");
+
+        let embed_tokens = embedding(cfg.vocab_size, cfg.d_model, vb.pp("embed_tokens"))?;
+        let embed_positions = TrOCRLearnedPositionalEmbedding::load(vb.pp("embed_positions"), cfg)?;
+        let mut layers = Vec::with_capacity(cfg.decoder_layers);
+        let vb_l = vb.pp("layers");
+        for idx in 0..cfg.decoder_layers {
+            let layer = TrOCRDecoderLayer::load(vb_l.pp(idx), cfg)?;
+            layers.push(layer)
+        }
+        let embed_scale = if cfg.scale_embedding {
+            Some((cfg.d_model as f64).sqrt())
+        } else {
+            None
+        };
+
+        Ok(Self {
+            layers,
+            embed_scale,
+            embed_tokens,
+            embed_positions,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.layers.iter_mut().for_each(|l| l.reset_kv_cache())
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: Option<&Tensor>,
+        past_kv_len: usize,
+        attn_mask: &Tensor,
+    ) -> Result<Tensor> {
+        let embed_pos = self.embed_positions.forward(xs, past_kv_len as u32)?;
+        let xs = xs.apply(&self.embed_tokens)?;
+
+        let xs = match self.embed_scale {
+            None => xs,
+            Some(scale) => (xs * scale)?,
+        };
+
+        let mut xs = xs.broadcast_add(&embed_pos)?;
+
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attn_mask, encoder_xs)?;
+        }
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCREncoder {
+    embeddings: Embeddings,
+    encoder: Encoder,
+    layernorm: LayerNorm,
+}
+
+impl TrOCREncoder {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_v = vb.pp("encoder");
+
+        let embeddings = Embeddings::new(cfg, false, vb_v.pp("embeddings"))?;
+
+        let encoder = Encoder::new(cfg, vb_v.pp("encoder"))?;
+        let layernorm = layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb_v.pp("layernorm"))?;
+
+        Ok(Self {
+            embeddings,
+            encoder,
+            layernorm,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let embedding_output = self.embeddings.forward(xs, None, false)?;
+        let encoder_outputs = self.encoder.forward(&embedding_output)?;
+
+        self.layernorm.forward(&encoder_outputs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRForCausalLM {
+    decoder: TrOCRDecoder,
+    output_projection: Linear,
+}
+
+impl TrOCRForCausalLM {
+    pub fn new(decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let decoder = TrOCRDecoder::new(decoder_cfg, vb.clone())?;
+        let output_projection =
+            candle_nn::Linear::new(decoder.embed_tokens.embeddings().clone(), None);
+        Ok(Self {
+            decoder,
+            output_projection,
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: Option<&Tensor>,
+        past_kv_len: usize,
+        attn_mask: &Tensor,
+    ) -> Result<Tensor> {
+        let xs = self
+            .decoder
+            .forward(xs, encoder_xs, past_kv_len, attn_mask)?;
+        let xs = xs.apply(&self.output_projection)?;
+
+        Ok(xs)
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.decoder.reset_kv_cache();
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRModel {
+    encoder: TrOCREncoder,
+    decoder: TrOCRForCausalLM,
+}
+
+impl TrOCRModel {
+    pub fn new(encoder_cfg: &Config, decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let encoder = TrOCREncoder::new(encoder_cfg, vb.clone())?;
+        let decoder = TrOCRForCausalLM::new(decoder_cfg, vb)?;
+        Ok(Self { encoder, decoder })
+    }
+
+    pub fn encoder(&mut self) -> &mut TrOCREncoder {
+        &mut self.encoder
+    }
+
+    pub fn decoder(&mut self) -> &mut TrOCRForCausalLM {
+        &mut self.decoder
+    }
+
+    pub fn decode(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: &Tensor,
+        past_kv_len: usize,
+    ) -> Result<Tensor> {
+        let seq_len = xs.dim(1)?;
+        let mask: Vec<_> = (0..seq_len)
+            .flat_map(|i| (0..seq_len).map(move |j| if j > i { f32::NEG_INFINITY } else { 0f32 }))
+            .collect();
+        let mask = Tensor::from_vec(mask, (seq_len, seq_len), xs.device())?;
+
+        self.decoder
+            .forward(xs, Some(encoder_xs), past_kv_len, &mask)
+    }
+
+    pub fn reset_kv_cache(&mut self) {
+        self.decoder.reset_kv_cache();
+    }
+}

candle-transformers/src/models/vit.rs

@@ -6,16 +6,16 @@ use candle_nn::{layer_norm, LayerNorm, VarBuilder};
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/configuration_vit.py
 #[derive(Debug, Clone)]
 pub struct Config {
-    hidden_size: usize,
-    num_hidden_layers: usize,
-    num_attention_heads: usize,
-    intermediate_size: usize,
-    hidden_act: candle_nn::Activation,
-    layer_norm_eps: f64,
-    image_size: usize,
-    patch_size: usize,
-    num_channels: usize,
-    qkv_bias: bool,
+    pub hidden_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub intermediate_size: usize,
+    pub hidden_act: candle_nn::Activation,
+    pub layer_norm_eps: f64,
+    pub image_size: usize,
+    pub patch_size: usize,
+    pub num_channels: usize,
+    pub qkv_bias: bool,
 }
 
 impl Config {
@@ -34,6 +34,21 @@ impl Config {
             qkv_bias: true,
         }
     }
+
+    pub fn microsoft_trocr_base_handwritten() -> Self {
+        Self {
+            hidden_size: 768,
+            num_hidden_layers: 12,
+            num_attention_heads: 12,
+            intermediate_size: 3072,
+            hidden_act: candle_nn::Activation::Gelu,
+            layer_norm_eps: 1e-12,
+            image_size: 384,
+            patch_size: 16,
+            num_channels: 3,
+            qkv_bias: false,
+        }
+    }
 }
 
 #[derive(Debug, Clone)]
@@ -76,7 +91,7 @@ impl Module for PatchEmbeddings {
 }
 
 #[derive(Debug, Clone)]
-struct Embeddings {
+pub struct Embeddings {
     cls_token: Tensor,
     mask_token: Option<Tensor>,
     patch_embeddings: PatchEmbeddings,
@@ -85,7 +100,7 @@ struct Embeddings {
 }
 
 impl Embeddings {
-    fn new(cfg: &Config, use_mask_token: bool, vb: VarBuilder) -> Result<Self> {
+    pub fn new(cfg: &Config, use_mask_token: bool, vb: VarBuilder) -> Result<Self> {
         let hidden_size = cfg.hidden_size;
         let cls_token = vb.get((1, 1, hidden_size), "cls_token")?;
         let mask_token = if use_mask_token {
@@ -115,7 +130,7 @@ impl Embeddings {
         todo!()
     }
 
-    fn forward(
+    pub fn forward(
         &self,
         pixel_values: &Tensor,
         bool_masked_pos: Option<&Tensor>,
@@ -324,12 +339,12 @@ impl Module for Layer {
 }
 
 #[derive(Debug, Clone)]
-struct Encoder {
+pub struct Encoder {
     layers: Vec<Layer>,
 }
 
 impl Encoder {
-    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
         let vb = vb.pp("layer");
         let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
         for i in 0..cfg.num_hidden_layers {

candle-transformers/src/models/whisper/audio.rs

@@ -58,8 +58,7 @@ fn dft<T: Float>(inp: &[T]) -> Vec<T> {
     let n = inp.len();
     let two_pi = T::PI() + T::PI();
 
-    let mut out = Vec::new();
-    out.reserve(2 * n);
+    let mut out = Vec::with_capacity(2 * n);
     let n_t = T::from(n).unwrap();
     for k in 0..n {
         let k_t = T::from(k).unwrap();

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

@@ -43,4 +43,4 @@ pub const TRANSCRIBE_TOKEN: &str = "<|transcribe|>";
 pub const TRANSLATE_TOKEN: &str = "<|translate|>";
 pub const NO_TIMESTAMPS_TOKEN: &str = "<|notimestamps|>";
 pub const EOT_TOKEN: &str = "<|endoftext|>";
-pub const NO_SPEECH_TOKEN: &str = "<|nocaptions|>";
+pub const NO_SPEECH_TOKENS: [&str; 2] = ["<|nocaptions|>", "<|nospeech|>"];

candle-transformers/src/models/yi.rs

@@ -0,0 +1,381 @@
+/// https://huggingface.co/01-ai/Yi-6B/blob/main/modeling_yi.py
+use crate::models::with_tracing::{linear_no_bias, Linear};
+use candle::{DType, Device, Module, Result, Tensor, D};
+use candle_nn::{Activation, VarBuilder};
+use std::sync::Arc;
+
+#[derive(Debug, Clone, PartialEq)]
+pub struct Config {
+    pub(crate) vocab_size: usize,
+    pub(crate) hidden_size: usize,
+    pub(crate) intermediate_size: usize,
+    pub(crate) num_hidden_layers: usize,
+    pub(crate) num_attention_heads: usize,
+    pub(crate) num_key_value_heads: usize,
+    pub(crate) hidden_act: Activation,
+    pub(crate) max_position_embeddings: usize,
+    pub(crate) rms_norm_eps: f64,
+    pub(crate) rope_theta: f64,
+}
+
+impl Config {
+    pub fn config_6b() -> Self {
+        Self {
+            vocab_size: 64000,
+            hidden_size: 4096,
+            intermediate_size: 11008,
+            num_hidden_layers: 32,
+            num_attention_heads: 32,
+            num_key_value_heads: 4,
+            hidden_act: Activation::Silu,
+            max_position_embeddings: 4096,
+            rms_norm_eps: 1e-5,
+            rope_theta: 5_000_000.,
+        }
+    }
+
+    pub fn config_34b() -> Self {
+        Self {
+            vocab_size: 64000,
+            hidden_size: 7168,
+            intermediate_size: 20480,
+            num_hidden_layers: 60,
+            num_attention_heads: 56,
+            num_key_value_heads: 8,
+            hidden_act: Activation::Silu,
+            max_position_embeddings: 4096,
+            rms_norm_eps: 1e-5,
+            rope_theta: 5_000_000.,
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RmsNorm {
+    inner: candle_nn::RmsNorm,
+    span: tracing::Span,
+}
+
+impl RmsNorm {
+    fn new(size: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "rms-norm");
+        let inner = candle_nn::rms_norm(size, eps, vb)?;
+        Ok(Self { inner, span })
+    }
+}
+
+impl Module for RmsNorm {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(x)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+fn rotate_half(xs: &Tensor) -> Result<Tensor> {
+    let last_dim = xs.dim(D::Minus1)?;
+    let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
+    let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
+    Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
+}
+
+impl RotaryEmbedding {
+    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
+        let dim = cfg.hidden_size / cfg.num_attention_heads;
+        let max_seq_len = cfg.max_position_embeddings;
+        let inv_freq: Vec<_> = (0..dim)
+            .step_by(2)
+            .map(|i| 1f32 / 10000f32.powf(i as f32 / dim 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)?;
+        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
+            .to_dtype(dtype)?
+            .reshape((max_seq_len, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
+        Ok(Self {
+            sin: freqs.sin()?,
+            cos: freqs.cos()?,
+        })
+    }
+
+    fn apply_rotary_emb_qkv(
+        &self,
+        q: &Tensor,
+        k: &Tensor,
+        seqlen_offset: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
+        let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
+        let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
+        let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
+        let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
+        let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
+        Ok((q_embed, k_embed))
+    }
+}
+
+#[derive(Debug, Clone)]
+#[allow(clippy::upper_case_acronyms)]
+struct MLP {
+    gate_proj: Linear,
+    up_proj: Linear,
+    down_proj: Linear,
+    act_fn: Activation,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let intermediate_sz = cfg.intermediate_size;
+        let gate_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("gate_proj"))?;
+        let up_proj = linear_no_bias(hidden_sz, intermediate_sz, vb.pp("up_proj"))?;
+        let down_proj = linear_no_bias(intermediate_sz, hidden_sz, vb.pp("down_proj"))?;
+        Ok(Self {
+            gate_proj,
+            up_proj,
+            down_proj,
+            act_fn: cfg.hidden_act,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
+        let rhs = xs.apply(&self.up_proj)?;
+        (lhs * rhs)?.apply(&self.down_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    num_heads: usize,
+    num_kv_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    hidden_size: usize,
+    rotary_emb: Arc<RotaryEmbedding>,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl Attention {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let num_heads = cfg.num_attention_heads;
+        let num_kv_heads = cfg.num_key_value_heads;
+        let num_kv_groups = num_heads / num_kv_heads;
+        let head_dim = hidden_sz / num_heads;
+        let q_proj = linear_no_bias(hidden_sz, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_no_bias(hidden_sz, num_kv_heads * head_dim, vb.pp("v_proj"))?;
+        let o_proj = linear_no_bias(num_heads * head_dim, hidden_sz, vb.pp("o_proj"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            num_heads,
+            num_kv_heads,
+            num_kv_groups,
+            head_dim,
+            hidden_size: hidden_sz,
+            rotary_emb,
+            kv_cache: None,
+        })
+    }
+
+    fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
+        let n_rep = self.num_kv_groups;
+        if n_rep == 1 {
+            Ok(xs)
+        } else {
+            let (b_sz, num_kv_heads, seq_len, head_dim) = xs.dims4()?;
+            xs.unsqueeze(2)?
+                .expand((b_sz, num_kv_heads, n_rep, seq_len, head_dim))?
+                .reshape((b_sz, num_kv_heads * n_rep, seq_len, head_dim))
+        }
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let (b_sz, q_len, _) = xs.dims3()?;
+
+        let query_states = self.q_proj.forward(xs)?;
+        let key_states = self.k_proj.forward(xs)?;
+        let value_states = self.v_proj.forward(xs)?;
+
+        let query_states = query_states
+            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let key_states = key_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let value_states = value_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+
+        let (query_states, key_states) =
+            self.rotary_emb
+                .apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
+
+        let (key_states, value_states) = match &self.kv_cache {
+            None => (key_states, value_states),
+            Some((prev_k, prev_v)) => {
+                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
+                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
+                (key_states, value_states)
+            }
+        };
+        self.kv_cache = Some((key_states.clone(), value_states.clone()));
+
+        let key_states = self.repeat_kv(key_states)?;
+        let value_states = self.repeat_kv(value_states)?;
+
+        let attn_output = {
+            let scale = 1f64 / f64::sqrt(self.head_dim as f64);
+            let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
+
+            let attn_weights = match attention_mask {
+                None => attn_weights,
+                Some(mask) => attn_weights.broadcast_add(mask)?,
+            };
+            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+            attn_weights.matmul(&value_states)?
+        };
+        attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, q_len, self.hidden_size))?
+            .apply(&self.o_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    ln1: RmsNorm,
+    ln2: RmsNorm,
+}
+
+impl DecoderLayer {
+    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        let ln1 = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
+        let ln2 = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            ln1,
+            ln2,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.ln1.forward(xs)?;
+        let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = xs.apply(&self.ln2)?.apply(&self.mlp)?;
+        residual + xs
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embed_tokens: candle_nn::Embedding,
+    layers: Vec<DecoderLayer>,
+    norm: RmsNorm,
+    lm_head: Linear,
+    device: Device,
+    dtype: DType,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        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 layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
+        let lm_head = linear_no_bias(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            lm_head,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+        })
+    }
+
+    fn prepare_decoder_attention_mask(
+        &self,
+        b_size: usize,
+        tgt_len: usize,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        // Sliding window mask?
+        let mask: Vec<_> = (0..tgt_len)
+            .flat_map(|i| (0..tgt_len).map(move |j| if i < j { 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)
+    }
+
+    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 mut xs = self.embed_tokens.forward(input_ids)?;
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
+        }
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm)?
+            .apply(&self.lm_head)
+    }
+}

candle-wasm-examples/bert/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/blip/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 num-traits = { workspace = true }
 

candle-wasm-examples/llama2-c/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/phi/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 num-traits = { workspace = true }
 

candle-wasm-examples/segment-anything/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 num-traits = { workspace = true }
 
 # App crates.

candle-wasm-examples/t5/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/whisper/Cargo.toml

@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../../candle-transformers", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.3.1" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/whisper/src/audio.rs

@@ -59,8 +59,7 @@ fn dft<T: Float>(inp: &[T]) -> Vec<T> {
     let n = inp.len();
     let two_pi = T::PI() + T::PI();
 
-    let mut out = Vec::new();
-    out.reserve(2 * n);
+    let mut out = Vec::with_capacity(2 * n);
     let n_t = T::from(n).unwrap();
     for k in 0..n {
         let k_t = T::from(k).unwrap();

candle-wasm-examples/whisper/src/worker.rs

@@ -129,7 +129,13 @@ impl Decoder {
         let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
         let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
         let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
-        let no_speech_token = token_id(&tokenizer, m::NO_SPEECH_TOKEN)?;
+        let no_speech_token = m::NO_SPEECH_TOKENS
+            .iter()
+            .find_map(|token| token_id(&tokenizer, token).ok());
+        let no_speech_token = match no_speech_token {
+            None => anyhow::bail!("unable to find any non-speech token"),
+            Some(n) => n,
+        };
         let seed = 299792458;
         Ok(Self {
             model,

candle-wasm-examples/yolo/Cargo.toml

@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.3.0" }
+candle = { path = "../../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.3.1" }
 num-traits = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

candle-wasm-tests/Cargo.toml

@@ -7,7 +7,7 @@ keywords.workspace = true
 categories.workspace = true
 
 [dependencies]
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
 rand = { workspace = true }
 getrandom = { version = "0.2", features = ["js"] }
 

candle-wasm-tests/tests/quantized_tests.rs

@@ -1,7 +1,7 @@
 use candle::{
     quantized::{self, k_quants, GgmlDType, GgmlType},
     test_utils::to_vec2_round,
-    Device, Result, Tensor,
+    Device, Module, Result, Tensor,
 };
 
 use wasm_bindgen_test::*;