more clppy

Author: Jeadie

candle-core/tests/quantized_tests.rs

@@ -6,7 +6,7 @@ use candle_core::{
     DType, Device, IndexOp, Module, Result, Tensor, Var,
 };
 use quantized::{k_quants, GgmlType};
-use rand::prelude::*;
+use rand::{prelude::*, random};
 
 const GGML_TEST_SIZE: usize = 32 * 128;
 
@@ -1110,13 +1110,11 @@ fn get_random_tensors(
     n: usize,
     device: &Device,
 ) -> Result<(Tensor, Tensor, Tensor)> {
-    let mut rng = StdRng::seed_from_u64(314159265358979);
-
     let lhs = (0..m * k)
-        .map(|_| rng.gen::<f32>() - 0.5)
+        .map(|_| random::<f32>() - 0.5)
         .collect::<Vec<_>>();
     let rhs = (0..n * k)
-        .map(|_| rng.gen::<f32>() - 0.5)
+        .map(|_| random::<f32>() - 0.5)
         .collect::<Vec<_>>();
 
     let lhs = Tensor::from_vec(lhs, (m, k), device)?;

candle-nn/src/loss.rs

@@ -7,7 +7,7 @@ use candle::{Result, Tensor};
 /// Arguments
 ///
 /// * [inp]: The input tensor of dimensions `N, C` where `N` is the batch size and `C` the number
-///          of categories. This is expected to contain log probabilities.
+///   of categories. This is expected to contain log probabilities.
 /// * [target]: The ground truth labels as a tensor of u32 of dimension `N`.
 ///
 /// The resulting tensor is a scalar containing the average value over the batch.
@@ -34,7 +34,7 @@ pub fn nll(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
 /// Arguments
 ///
 /// * [inp]: The input tensor of dimensions `N, C` where `N` is the batch size and `C` the number
-///          of categories. This is expected to raw logits.
+///   of categories. This is expected to raw logits.
 /// * [target]: The ground truth labels as a tensor of u32 of dimension `N`.
 ///
 /// The resulting tensor is a scalar containing the average value over the batch.
@@ -56,9 +56,9 @@ pub fn mse(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
 /// Arguments
 ///
 /// * [inp]: The input tensor of dimensions `N, C` where `N` is the batch size and `C` the number
-///          of categories. This is expected to raw logits.
+///   of categories. This is expected to raw logits.
 /// * [target]: The ground truth labels as a tensor of u32 of dimension `N, C` where `N` is the batch size and `C` the number
-///          of categories.
+///   of categories.
 ///
 /// The resulting tensor is a scalar containing the average value over the batch.
 pub fn binary_cross_entropy_with_logit(inp: &Tensor, target: &Tensor) -> Result<Tensor> {