Lint fixes introduced with Rust 1.83 (#2646)

* Fixes for lint errors introduced with Rust 1.83

* rustfmt

* Fix more lints.

---------

Co-authored-by: Laurent <laurent.mazare@gmail.com>

candle-core/src/cpu_backend/mod.rs

@@ -66,7 +66,7 @@ impl Map2U8 for Cmp {
 
 struct WCond<'a, T: IntDType>(&'a [T], &'a Layout);
 
-impl<'a, I: IntDType> Map2 for WCond<'a, I> {
+impl<I: IntDType> Map2 for WCond<'_, I> {
     const OP: &'static str = "where";
     #[inline(always)]
     fn f<T: WithDType>(&self, t: &[T], t_l: &Layout, f: &[T], f_l: &Layout) -> Result<Vec<T>> {
@@ -216,7 +216,7 @@ struct ReduceSum<'a> {
     reduce_dims_and_stride: Vec<(usize, usize)>,
 }
 
-impl<'a> ReduceSum<'a> {
+impl ReduceSum<'_> {
     #[inline(always)]
     fn fold_impl<T>(&self, src: &[T], src_l: &Layout, start_elt: T) -> Result<Vec<T>>
     where
@@ -281,7 +281,7 @@ impl<'a> ReduceSum<'a> {
     }
 }
 
-impl<'a> Map1 for ReduceSum<'a> {
+impl Map1 for ReduceSum<'_> {
     #[inline(always)]
     fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
         self.fold_impl(src, src_l, T::zero())
@@ -454,7 +454,7 @@ struct Gather<'a, I: IntDType> {
     dim: usize,
 }
 
-impl<'a, I: IntDType> Map1 for Gather<'a, I> {
+impl<I: IntDType> Map1 for Gather<'_, I> {
     fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
         let ids = match self.ids_l.contiguous_offsets() {
             Some((a, b)) => &self.ids[a..b],
@@ -507,7 +507,7 @@ struct IndexSelect<'a, T: IntDType> {
     dim: usize,
 }
 
-impl<'a, I: IntDType> Map1 for IndexSelect<'a, I> {
+impl<I: IntDType> Map1 for IndexSelect<'_, I> {
     fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
         let src = match layout.contiguous_offsets() {
             Some((a, b)) => &src[a..b],
@@ -560,7 +560,7 @@ struct ScatterAdd<'a, I: IntDType> {
     dim: usize,
 }
 
-impl<'a, I: IntDType> Map2 for ScatterAdd<'a, I> {
+impl<I: IntDType> Map2 for ScatterAdd<'_, I> {
     const OP: &'static str = "scatter-add";
     fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
         let dst_len = l1.shape().elem_count();
@@ -616,7 +616,7 @@ struct IndexAdd<'a, I: IntDType> {
     dim: usize,
 }
 
-impl<'a, I: IntDType> Map2 for IndexAdd<'a, I> {
+impl<I: IntDType> Map2 for IndexAdd<'_, I> {
     const OP: &'static str = "index-add";
     // https://pytorch.org/docs/stable/generated/torch.Tensor.index_add_.html#torch.Tensor.index_add_
     // v1, l1 -> self
@@ -736,7 +736,7 @@ fn copy_strided_src_<T: Copy>(src: &[T], dst: &mut [T], dst_offset: usize, src_l
 
 struct Conv1D<'a>(&'a crate::conv::ParamsConv1D);
 
-impl<'a> Map2 for Conv1D<'a> {
+impl Map2 for Conv1D<'_> {
     const OP: &'static str = "conv1d";
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;
@@ -960,7 +960,7 @@ impl Map1 for Col2Im1D {
 
 struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
 
-impl<'a> Map2 for ConvTranspose1D<'a> {
+impl Map2 for ConvTranspose1D<'_> {
     const OP: &'static str = "conv_transpose1d";
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;
@@ -1029,7 +1029,7 @@ impl<'a> Map2 for ConvTranspose1D<'a> {
 
 struct Conv2D<'a>(&'a crate::conv::ParamsConv2D);
 
-impl<'a> Map2 for Conv2D<'a> {
+impl Map2 for Conv2D<'_> {
     const OP: &'static str = "conv2d";
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;
@@ -1117,7 +1117,7 @@ impl<'a> Map2 for Conv2D<'a> {
 
 struct ConvTranspose2D<'a>(&'a crate::conv::ParamsConvTranspose2D);
 
-impl<'a> Map2 for ConvTranspose2D<'a> {
+impl Map2 for ConvTranspose2D<'_> {
     const OP: &'static str = "conv_transpose2d";
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;

candle-core/src/quantized/gguf_file.rs

@@ -457,7 +457,7 @@ impl Content {
             Some(Value::I32(v)) if *v >= 0 => *v as u64,
             _ => DEFAULT_ALIGNMENT,
         };
-        let tensor_data_offset = (position + alignment - 1) / alignment * alignment;
+        let tensor_data_offset = position.div_ceil(alignment) * alignment;
         Ok(Self {
             magic,
             metadata,

candle-core/src/quantized/k_quants.rs

@@ -1850,8 +1850,8 @@ pub fn matmul<T: GgmlType>(
         crate::bail!("unexpected lhs length {} {mkn:?}", lhs.len());
     }
 
-    let k_in_lhs_blocks = (k + T::BLCK_SIZE - 1) / T::BLCK_SIZE;
-    let k_in_rhs_blocks = (k + T::VecDotType::BLCK_SIZE - 1) / T::VecDotType::BLCK_SIZE;
+    let k_in_lhs_blocks = k.div_ceil(T::BLCK_SIZE);
+    let k_in_rhs_blocks = k.div_ceil(T::VecDotType::BLCK_SIZE);
     // TODO: Do not make this copy if the DotType is f32.
     // TODO: Pre-allocate this.
     let mut lhs_b = vec![T::VecDotType::zeros(); m * k_in_lhs_blocks];

candle-core/src/safetensors.rs

@@ -182,7 +182,7 @@ pub trait Load {
     fn load(&self, device: &Device) -> Result<Tensor>;
 }
 
-impl<'a> Load for st::TensorView<'a> {
+impl Load for st::TensorView<'_> {
     fn load(&self, device: &Device) -> Result<Tensor> {
         convert(self, device)
     }

candle-core/src/strided_index.rs

@@ -32,7 +32,7 @@ impl<'a> StridedIndex<'a> {
     }
 }
 
-impl<'a> Iterator for StridedIndex<'a> {
+impl Iterator for StridedIndex<'_> {
     type Item = usize;
 
     fn next(&mut self) -> Option<Self::Item> {

candle-datasets/src/nlp/tinystories.rs

@@ -87,7 +87,7 @@ impl<'a> DatasetRandomIter<'a> {
     }
 }
 
-impl<'a> Iterator for DatasetRandomIter<'a> {
+impl Iterator for DatasetRandomIter<'_> {
     type Item = Result<(Tensor, Tensor)>;
 
     fn next(&mut self) -> Option<Self::Item> {

candle-examples/examples/mamba-minimal/model.rs

@@ -17,7 +17,7 @@ pub struct Config {
 impl Config {
     fn vocab_size(&self) -> usize {
         let pad = self.pad_vocab_size_multiple;
-        (self.vocab_size + pad - 1) / pad * pad
+        self.vocab_size.div_ceil(pad) * pad
     }
 
     fn dt_rank(&self) -> usize {

candle-examples/src/imagenet.rs

@@ -6,7 +6,6 @@ pub const IMAGENET_STD: [f32; 3] = [0.229f32, 0.224, 0.225];
 /// Loads an image from disk using the image crate at the requested resolution,
 /// using the given std and mean parameters.
 /// This returns a tensor with shape (3, res, res). imagenet normalization is applied.
-
 pub fn load_image_with_std_mean<P: AsRef<std::path::Path>>(
     p: P,
     res: usize,

candle-metal-kernels/src/lib.rs

@@ -372,7 +372,7 @@ pub fn call_unary_contiguous_tiled(
     let encoder = ep.encoder();
     let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
     let tile_size = 2;
-    let tiles = (length + tile_size - 1) / tile_size;
+    let tiles = length.div_ceil(tile_size);
 
     encoder.set_compute_pipeline_state(&pipeline);
 
@@ -594,7 +594,7 @@ pub fn call_reduce_contiguous(
 
     let width = std::cmp::min(
         pipeline.max_total_threads_per_threadgroup(),
-        (elements_to_sum as u64 + 2 - 1) / 2,
+        (elements_to_sum as u64).div_ceil(2),
     )
     .next_power_of_two();
 
@@ -1735,7 +1735,7 @@ pub fn call_sdpa_full(
         }
     };
 
-    let pipeline = kernels.load_pipeline(device, Source::Sdpa, &name)?;
+    let pipeline = kernels.load_pipeline(device, Source::Sdpa, name)?;
     let encoder = ep.encoder();
     let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
     encoder.set_compute_pipeline_state(&pipeline);
@@ -1759,16 +1759,16 @@ pub fn call_sdpa_full(
     let ldo = dk;
 
     let tn = 1;
-    let tm = (m + BM - 1) / BM;
+    let tm = m.div_ceil(BM);
 
     let b_stride_q = dk * qseq;
     let b_stride_k = dk * qseq;
     let b_stride_v = dk * qseq;
     let b_stride_o = dk * qseq;
     let swizzle_log = 0;
-    let gemm_n_iterations_aligned = (n + BN - 1) / BN;
-    let gemm_k_iterations_aligned = (k + bk - 1) / bk;
-    let gemm_sv_m_block_iterations = (m + BM - 1) / BM;
+    let gemm_n_iterations_aligned = n.div_ceil(BN);
+    let gemm_k_iterations_aligned = k.div_ceil(*bk);
+    let gemm_sv_m_block_iterations = m.div_ceil(BM);
     let batch_ndim = batch_shape.len();
 
     let alpha = if softcapping != 1. {
@@ -1906,7 +1906,7 @@ pub fn call_sdpa_vector(
         alpha
     };
 
-    let pipeline = kernels.load_pipeline(device, Source::Sdpa, &name)?;
+    let pipeline = kernels.load_pipeline(device, Source::Sdpa, name)?;
     let encoder = ep.encoder();
     let encoder: &ComputeCommandEncoderRef = encoder.as_ref();
     encoder.set_compute_pipeline_state(&pipeline);
@@ -1933,7 +1933,7 @@ pub fn call_sdpa_vector(
     let grid_dims = MTLSize {
         width: 1,
         height: b as u64,
-        depth: 1 as u64,
+        depth: 1_u64,
     };
     let group_dims = MTLSize {
         width: 1024,
@@ -2320,7 +2320,7 @@ pub fn call_quantized_matmul_mv_t(
 }
 
 fn divide(m: usize, b: usize) -> NSUInteger {
-    ((m + b - 1) / b) as NSUInteger
+    m.div_ceil(b) as NSUInteger
 }
 
 #[allow(clippy::too_many_arguments)]

candle-metal-kernels/src/utils.rs

@@ -8,7 +8,7 @@ use std::ffi::c_void;
 pub(crate) fn linear_split(pipeline: &ComputePipelineState, length: usize) -> (MTLSize, MTLSize) {
     let size = length as u64;
     let width = std::cmp::min(pipeline.max_total_threads_per_threadgroup(), size);
-    let count = (size + width - 1) / width;
+    let count = size.div_ceil(width);
     let thread_group_count = MTLSize {
         width: count,
         height: 1,
@@ -128,7 +128,7 @@ impl EncoderParam for (&Buffer, usize) {
     }
 }
 
-impl<'a> EncoderParam for &BufferOffset<'a> {
+impl EncoderParam for &BufferOffset<'_> {
     fn set_param(encoder: &ComputeCommandEncoderRef, position: u64, data: Self) {
         encoder.set_buffer(position, Some(data.buffer), data.offset_in_bytes as u64);
     }
@@ -169,7 +169,7 @@ pub struct WrappedEncoder<'a> {
     end_encoding_on_drop: bool,
 }
 
-impl<'a> Drop for WrappedEncoder<'a> {
+impl Drop for WrappedEncoder<'_> {
     fn drop(&mut self) {
         if self.end_encoding_on_drop {
             self.inner.end_encoding()
@@ -177,14 +177,15 @@ impl<'a> Drop for WrappedEncoder<'a> {
     }
 }
 
-impl<'a> AsRef<metal::ComputeCommandEncoderRef> for WrappedEncoder<'a> {
+impl AsRef<metal::ComputeCommandEncoderRef> for WrappedEncoder<'_> {
     fn as_ref(&self) -> &metal::ComputeCommandEncoderRef {
         self.inner
     }
 }
 
 impl EncoderProvider for &metal::CommandBuffer {
-    type Encoder<'a> = WrappedEncoder<'a>
+    type Encoder<'a>
+        = WrappedEncoder<'a>
     where
         Self: 'a;
     fn encoder(&self) -> Self::Encoder<'_> {
@@ -196,7 +197,8 @@ impl EncoderProvider for &metal::CommandBuffer {
 }
 
 impl EncoderProvider for &metal::CommandBufferRef {
-    type Encoder<'a> = WrappedEncoder<'a>
+    type Encoder<'a>
+        = WrappedEncoder<'a>
     where
         Self: 'a;
     fn encoder(&self) -> Self::Encoder<'_> {
@@ -208,7 +210,8 @@ impl EncoderProvider for &metal::CommandBufferRef {
 }
 
 impl EncoderProvider for &ComputeCommandEncoderRef {
-    type Encoder<'a> = WrappedEncoder<'a>
+    type Encoder<'a>
+        = WrappedEncoder<'a>
     where
         Self: 'a;
     fn encoder(&self) -> Self::Encoder<'_> {

candle-nn/src/func.rs

@@ -9,7 +9,7 @@ pub struct Func<'a> {
     f: Arc<dyn 'a + Fn(&Tensor) -> Result<Tensor> + Send + Sync>,
 }
 
-impl<'a> std::fmt::Debug for Func<'a> {
+impl std::fmt::Debug for Func<'_> {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         write!(f, "func")
     }
@@ -22,7 +22,7 @@ where
     Func { f: Arc::new(f) }
 }
 
-impl<'a> super::Module for Func<'a> {
+impl super::Module for Func<'_> {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         (*self.f)(xs)
     }
@@ -44,7 +44,7 @@ pub struct FuncT<'a> {
     f: Arc<dyn 'a + Fn(&Tensor, bool) -> Result<Tensor> + Send + Sync>,
 }
 
-impl<'a> std::fmt::Debug for FuncT<'a> {
+impl std::fmt::Debug for FuncT<'_> {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         write!(f, "func")
     }
@@ -57,7 +57,7 @@ where
     FuncT { f: Arc::new(f) }
 }
 
-impl<'a> super::ModuleT for FuncT<'a> {
+impl super::ModuleT for FuncT<'_> {
     fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor> {
         (*self.f)(xs, train)
     }

candle-nn/src/var_builder.rs

@@ -20,7 +20,7 @@ pub struct VarBuilderArgs<'a, B: Backend> {
     _phantom: std::marker::PhantomData<&'a B>,
 }
 
-impl<'a, B: Backend> Clone for VarBuilderArgs<'a, B> {
+impl<B: Backend> Clone for VarBuilderArgs<'_, B> {
     fn clone(&self) -> Self {
         Self {
             data: self.data.clone(),
@@ -76,7 +76,7 @@ pub trait SimpleBackend: Send + Sync {
     fn contains_tensor(&self, name: &str) -> bool;
 }
 
-impl<'a> Backend for Box<dyn SimpleBackend + 'a> {
+impl Backend for Box<dyn SimpleBackend + '_> {
     type Hints = crate::Init;
     fn get(
         &self,
@@ -94,7 +94,7 @@ impl<'a> Backend for Box<dyn SimpleBackend + 'a> {
     }
 }
 
-impl<'a, B: Backend> VarBuilderArgs<'a, B> {
+impl<B: Backend> VarBuilderArgs<'_, B> {
     pub fn new_with_args(backend: B, dtype: DType, dev: &Device) -> Self {
         let data = TensorData {
             backend,
@@ -286,7 +286,7 @@ pub struct SafeTensorWithRouting<'a> {
     safetensors: Vec<SafeTensors<'a>>,
 }
 
-impl<'a> SimpleBackend for SafeTensorWithRouting<'a> {
+impl SimpleBackend for SafeTensorWithRouting<'_> {
     fn get(
         &self,
         s: Shape,
@@ -439,7 +439,7 @@ impl SimpleBackend for candle::safetensors::BufferedSafetensors {
     }
 }
 
-impl<'a> SimpleBackend for candle::safetensors::SliceSafetensors<'a> {
+impl SimpleBackend for candle::safetensors::SliceSafetensors<'_> {
     fn get(
         &self,
         s: Shape,
@@ -732,7 +732,7 @@ pub struct Rename<'a, R: Renamer> {
     renamer: R,
 }
 
-impl<'a, R: Renamer + Sync + Send> SimpleBackend for Rename<'a, R> {
+impl<R: Renamer + Sync + Send> SimpleBackend for Rename<'_, R> {
     fn get(
         &self,
         s: Shape,

candle-pyo3/src/lib.rs

@@ -276,7 +276,7 @@ impl PyTensor {
     /// &RETURNS&: _ArrayLike
     fn values(&self, py: Python<'_>) -> PyResult<PyObject> {
         struct M<'a>(Python<'a>);
-        impl<'a> MapDType for M<'a> {
+        impl MapDType for M<'_> {
             type Output = PyObject;
             fn f<T: PyWithDType>(&self, t: &Tensor) -> PyResult<Self::Output> {
                 match t.rank() {

candle-transformers/src/models/convmixer.rs

@@ -21,8 +21,8 @@ fn conv2d_same(
     let module = candle_nn::func(move |xs| {
         let ih = xs.dim(2)?;
         let iw = xs.dim(3)?;
-        let oh = (ih + s - 1) / s;
-        let ow = (iw + s - 1) / s;
+        let oh = ih.div_ceil(s);
+        let ow = iw.div_ceil(s);
         let pad_h = usize::max((oh - 1) * s + k - ih, 0);
         let pad_w = usize::max((ow - 1) * s + k - iw, 0);
         if pad_h > 0 || pad_w > 0 {

candle-transformers/src/models/depth_anything_v2.rs

@@ -543,7 +543,7 @@ impl<'a> DepthAnythingV2<'a> {
     }
 }
 
-impl<'a> Module for DepthAnythingV2<'a> {
+impl Module for DepthAnythingV2<'_> {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let features = self.pretrained.get_intermediate_layers(
             xs,

candle-transformers/src/models/efficientnet.rs

@@ -125,8 +125,8 @@ impl Module for Conv2DSame {
         let s = self.s;
         let k = self.k;
         let (_, _, ih, iw) = xs.dims4()?;
-        let oh = (ih + s - 1) / s;
-        let ow = (iw + s - 1) / s;
+        let oh = ih.div_ceil(s);
+        let ow = iw.div_ceil(s);
         let pad_h = usize::max((oh - 1) * s + k - ih, 0);
         let pad_w = usize::max((ow - 1) * s + k - iw, 0);
         if pad_h > 0 || pad_w > 0 {

candle-transformers/src/models/encodec.rs

@@ -89,7 +89,7 @@ impl Config {
 
     fn frame_rate(&self) -> usize {
         let hop_length: usize = self.upsampling_ratios.iter().product();
-        (self.sampling_rate + hop_length - 1) / hop_length
+        self.sampling_rate.div_ceil(hop_length)
     }
 
     fn num_quantizers(&self) -> usize {

candle-transformers/src/models/mamba.rs

@@ -23,7 +23,7 @@ pub struct Config {
 impl Config {
     fn vocab_size(&self) -> usize {
         let pad = self.pad_vocab_size_multiple;
-        (self.vocab_size + pad - 1) / pad * pad
+        self.vocab_size.div_ceil(pad) * pad
     }
 
     fn dt_rank(&self) -> usize {

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

@@ -21,7 +21,7 @@ struct LinearInterpolator<'x, 'y> {
     cache: usize,
 }
 
-impl<'x, 'y> LinearInterpolator<'x, 'y> {
+impl LinearInterpolator<'_, '_> {
     fn accel_find(&mut self, x: f64) -> usize {
         let xidx = self.cache;
         if x < self.xp[xidx] {