Chore: Fix a ton of warnings

Author: frjnn

crates/cudnn/src/activation/activation_descriptor.rs

@@ -1,4 +1,4 @@
-use crate::{sys, ActivationMode, CudnnContext, CudnnError, IntoResult, NanPropagation};
+use crate::{sys, ActivationMode, CudnnError, IntoResult, NanPropagation};
 use std::mem::MaybeUninit;
 
 /// The descriptor of a neuron activation operation.
@@ -47,7 +47,7 @@ impl ActivationDescriptor {
         unsafe {
             sys::cudnnCreateActivationDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             let coefficient = coefficient.into().unwrap_or_else(|| match mode {
                 ActivationMode::ClippedRelu => std::f64::MAX,

crates/cudnn/src/activation/mod.rs

@@ -8,7 +8,6 @@ use crate::{
     private, sys, CudnnContext, CudnnError, DataType, IntoResult, ScalingDataType, TensorDescriptor,
 };
 use cust::memory::GpuBuffer;
-use std::mem::MaybeUninit;
 
 impl CudnnContext {
     /// Applies a specific neuron activation functions element wise of the provided tensor.

crates/cudnn/src/attention/attention_descriptor.rs

@@ -125,7 +125,7 @@ where
         unsafe {
             sys::cudnnCreateAttnDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             sys::cudnnSetAttnDescriptor(
                 raw,

crates/cudnn/src/attention/mod.rs

@@ -8,7 +8,7 @@ pub use attention_weights_kind::*;
 pub use seq_data_axis::*;
 pub use seq_data_descriptor::*;
 
-use crate::{sys, CudnnContext, CudnnError, DataType, IntoResult, WGradMode};
+use crate::{sys, CudnnContext, CudnnError, IntoResult, WGradMode};
 use cust::memory::GpuBuffer;
 use std::mem::MaybeUninit;
 
@@ -259,7 +259,6 @@ impl CudnnContext {
     pub fn multi_head_attn_backward_data<T, U, D1, D2>(
         &self,
         attn_desc: &AttentionDescriptor<T, U, D1, D2>,
-        current_idx: i32,
         lo_win_idx: &[i32],
         hi_win_idx: &[i32],
         device_seq_lengths_dqdo: &impl GpuBuffer<i32>,
@@ -288,7 +287,7 @@ impl CudnnContext {
         let device_seq_lengths_dqdo_ptr =
             device_seq_lengths_dqdo.as_device_ptr().as_ptr() as *const _;
         let device_seq_lengths_dkdv_ptr =
-            device_seq_lengths_dqdo.as_device_ptr().as_ptr() as *const _;
+            device_seq_lengths_dkdv.as_device_ptr().as_ptr() as *const _;
 
         let d_out_ptr = d_out.as_device_ptr().as_ptr() as *const _;
 

crates/cudnn/src/attention/seq_data_descriptor.rs

@@ -120,7 +120,7 @@ where
         unsafe {
             sys::cudnnCreateSeqDataDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             let raw_axes = axes.map(SeqDataAxis::into);
 

crates/cudnn/src/backend/mod.rs

@@ -1,3 +1,5 @@
+#![allow(warnings)]
+
 mod conv_bwd_data;
 mod conv_bwd_filter;
 mod conv_cfg;

crates/cudnn/src/context.rs

@@ -1,6 +1,5 @@
 use crate::{sys, CudnnError, IntoResult};
-use cust::memory::{GpuBox, GpuBuffer};
-use std::mem::{self, MaybeUninit};
+use std::mem::MaybeUninit;
 
 /// cuDNN library context. It's the central structure required to interact with cuDNN.
 /// It holds and manages internal memory allocations.

crates/cudnn/src/convolution/convolution_algo.rs

@@ -1,10 +1,4 @@
-use crate::{
-    data_type::DataType,
-    determinism::Determinism,
-    error::{CudnnError, IntoResult},
-    math_type::MathType,
-    private, sys, TensorFormat,
-};
+use crate::{sys, CudnnError, Determinism, IntoResult, MathType};
 
 /// The best suited algorithm according to the layer specifications obtained through a heuristic.
 #[derive(Debug, Clone, Copy, PartialEq)]

crates/cudnn/src/convolution/convolution_config.rs

@@ -1,4 +1,4 @@
-use crate::{private, sys, DataType};
+use crate::{private, DataType};
 
 /// Supported data types configurations for convolution operations.
 pub trait SupportedConv<X, W, Y>: private::Sealed + DataType

crates/cudnn/src/convolution/convolution_descriptor.rs

@@ -1,6 +1,4 @@
-use crate::{
-    sys, ConvMode, DataType, MathType, TensorFormat, {CudnnError, IntoResult},
-};
+use crate::{sys, ConvMode, CudnnError, DataType, IntoResult, MathType};
 
 use std::{marker::PhantomData, mem::MaybeUninit};
 
@@ -97,7 +95,7 @@ impl<T: DataType> ConvDescriptor<T> {
         unsafe {
             sys::cudnnCreateConvolutionDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut conv_desc = Self {
+            let conv_desc = Self {
                 raw: raw.assume_init(),
                 comp_type: PhantomData,
             };

crates/cudnn/src/convolution/filter_descriptor.rs

@@ -1,8 +1,5 @@
 use crate::{sys, CudnnError, DataType, IntoResult, ScalarC};
-use std::{
-    marker::PhantomData,
-    mem::{self, MaybeUninit},
-};
+use std::{marker::PhantomData, mem::MaybeUninit};
 
 /// A generic description of an n-dimensional filter dataset.
 #[derive(Debug, PartialEq, Eq, Hash)]
@@ -58,6 +55,7 @@ where
 
         unsafe {
             sys::cudnnCreateFilterDescriptor(raw.as_mut_ptr()).into_result()?;
+
             let raw = raw.assume_init();
 
             sys::cudnnSetFilterNdDescriptor(

crates/cudnn/src/dropout/dropout_descriptor.rs

@@ -1,4 +1,4 @@
-use crate::{error::CudnnError, sys, IntoResult};
+use crate::sys;
 use cust::memory::GpuBuffer;
 
 /// The descriptor of a dropout operation.

crates/cudnn/src/dropout/mod.rs

@@ -67,19 +67,11 @@ impl CudnnContext {
     ///
     /// let ctx = CudnnContext::new()?;
     ///
-    /// let desc = TensorDescriptor::<f32>::new_strides(&[1, 1, 5], &[5, 5, 1])?;
-    ///
-    /// let size = ctx.get_dropout_reserved_space_size(&desc)?;
+    /// let size = ctx.get_dropout_reserved_space_size()?;
     /// # Ok(())
     /// # }
     /// ```
-    pub fn get_dropout_reserved_space_size<T>(
-        &self,
-        x_desc: &TensorDescriptor<T>,
-    ) -> Result<usize, CudnnError>
-    where
-        T: DataType,
-    {
+    pub fn get_dropout_reserved_space_size(&self) -> Result<usize, CudnnError> {
         let mut size = MaybeUninit::uninit();
 
         unsafe {
@@ -141,7 +133,7 @@ impl CudnnContext {
         unsafe {
             sys::cudnnCreateDropoutDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             sys::cudnnSetDropoutDescriptor(raw, self.raw, dropout, states_ptr, states_size, seed)
                 .into_result()?;
@@ -208,7 +200,7 @@ impl CudnnContext {
     /// let dropout_desc = ctx.create_dropout_descriptor(dropout, states, seed)?;
     ///
     /// let mut reserved_space = {
-    ///     let size = ctx.get_dropout_reserved_space_size(&x_desc)?;
+    ///     let size = ctx.get_dropout_reserved_space_size()?;
     ///     unsafe { DeviceBuffer::uninitialized(size)? }
     /// };
     ///
@@ -307,7 +299,7 @@ impl CudnnContext {
     /// # let seed = 123;
     /// # let dropout_desc = ctx.create_dropout_descriptor(dropout, states, seed)?;
     /// # let mut reserved_space = {
-    /// #     let size = ctx.get_dropout_reserved_space_size(&x_desc)?;
+    /// #     let size = ctx.get_dropout_reserved_space_size()?;
     /// #     unsafe { DeviceBuffer::uninitialized(size)? }
     /// # };
     /// # ctx.dropout_forward(&dropout_desc, &x_desc, &x, &y_desc, &mut y, &mut reserved_space)?;

crates/cudnn/src/error.rs

@@ -1,4 +1,4 @@
-use crate::sys::{self, cudnnStatus_t};
+use crate::sys;
 use std::{error::Error, ffi::CStr, fmt::Display};
 
 /// Enum encapsulating function status returns. All cuDNN library functions return their status.

crates/cudnn/src/lib.rs

@@ -1,6 +1,3 @@
-#![allow(warnings, clippy::all)]
-mod sys;
-
 mod activation;
 mod attention;
 mod backend;
@@ -16,6 +13,7 @@ mod op;
 mod pooling;
 mod rnn;
 mod softmax;
+mod sys;
 mod tensor;
 mod w_grad_mode;
 

crates/cudnn/src/op/op_tensor_descriptor.rs

@@ -18,7 +18,7 @@ unsafe fn init_raw_op_descriptor<T: DataType>(
 
     sys::cudnnCreateOpTensorDescriptor(raw.as_mut_ptr()).into_result()?;
 
-    let mut raw = raw.assume_init();
+    let raw = raw.assume_init();
 
     sys::cudnnSetOpTensorDescriptor(raw, op, T::into_raw(), nan_opt).into_result()?;
     Ok(raw)

crates/cudnn/src/pooling/pooling_descriptor.rs

@@ -60,7 +60,7 @@ impl PoolingDescriptor {
         unsafe {
             sys::cudnnCreatePoolingDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             sys::cudnnSetPoolingNdDescriptor(
                 raw,

crates/cudnn/src/rnn/mod.rs

@@ -20,7 +20,7 @@ pub use rnn_direction_mode::*;
 pub use rnn_input_mode::*;
 pub use rnn_mode::*;
 
-use crate::{sys, CudnnContext, CudnnError, DataType, IntoResult, TensorDescriptor, WGradMode};
+use crate::{sys, CudnnContext, CudnnError, IntoResult, TensorDescriptor, WGradMode};
 use cust::memory::GpuBuffer;
 use std::mem::MaybeUninit;
 
@@ -565,7 +565,7 @@ impl CudnnContext {
         let device_sequence_lengths_ptr = device_seq_lengths.as_device_ptr().as_mut_ptr();
 
         let x_ptr = x.as_device_ptr().as_ptr() as *const std::ffi::c_void;
-        let hx_ptr = x.as_device_ptr().as_ptr() as *const std::ffi::c_void;
+        let hx_ptr = hx.as_device_ptr().as_ptr() as *const std::ffi::c_void;
         let y_ptr = y.as_device_ptr().as_ptr() as *const std::ffi::c_void;
 
         let dweight_space_ptr = dweight_space.as_device_ptr().as_mut_ptr() as *mut std::ffi::c_void;

crates/cudnn/src/rnn/rnn_data_descriptor.rs

@@ -112,7 +112,7 @@ where
         unsafe {
             sys::cudnnCreateRNNDataDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             let fill: *mut T = padding_fill
                 .into()

crates/cudnn/src/rnn/rnn_data_layout.rs

@@ -1,4 +1,4 @@
-use crate::{sys, RnnDataDescriptor};
+use crate::sys;
 
 /// The data layout for input and output of a recurrent neural network.
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]

crates/cudnn/src/rnn/rnn_descriptor.rs

@@ -160,7 +160,7 @@ where
         unsafe {
             sys::cudnnCreateRNNDescriptor(raw.as_mut_ptr()).into_result()?;
 
-            let mut raw = raw.assume_init();
+            let raw = raw.assume_init();
 
             let proj_size = projection_size.into().unwrap_or(0);
             let dropout_desc = dropout_desc.map_or(std::ptr::null_mut(), |desc| desc.raw);

crates/cudnn/src/softmax/softmax_mode.rs

@@ -1,4 +1,4 @@
-use crate::{sys, SoftmaxAlgo};
+use crate::sys;
 
 /// Specifies how the softmax input must be processed.
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]

crates/cudnn/src/sys.rs

@@ -1,4 +1,5 @@
 /* automatically generated by rust-bindgen 0.59.2 */
+#![allow(warnings)]
 
 pub const CUDNN_MAJOR: u32 = 8;
 pub const CUDNN_MINOR: u32 = 3;

crates/cudnn/src/tensor/tensor_descriptor.rs

@@ -1,8 +1,5 @@
 use crate::{sys, CudnnError, DataType, IntoResult, ScalarC, TensorFormat, VecType};
-use std::{
-    marker::PhantomData,
-    mem::{self, MaybeUninit},
-};
+use std::{marker::PhantomData, mem::MaybeUninit};
 
 /// A generic description of an n-dimensional dataset.
 #[derive(Debug, PartialEq, Eq, Hash)]