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-rw-r--r--candle-core/src/lib.rs1
-rw-r--r--candle-core/src/test_utils.rs (renamed from candle-core/tests/test_utils.rs)15
-rw-r--r--candle-core/tests/conv_tests.rs3
-rw-r--r--candle-core/tests/custom_op_tests.rs4
-rw-r--r--candle-core/tests/grad_tests.rs3
-rw-r--r--candle-core/tests/indexing_tests.rs2
-rw-r--r--candle-core/tests/layout_tests.rs3
-rw-r--r--candle-core/tests/pool_tests.rs10
-rw-r--r--candle-core/tests/quantized_tests.rs3
-rw-r--r--candle-core/tests/tensor_tests.rs3
-rw-r--r--candle-nn/tests/batch_norm.rs4
-rw-r--r--candle-nn/tests/group_norm.rs7
-rw-r--r--candle-nn/tests/layer_norm.rs8
-rw-r--r--candle-nn/tests/loss.rs3
-rw-r--r--candle-nn/tests/ops.rs11
-rw-r--r--candle-nn/tests/optim.rs3
-rw-r--r--candle-nn/tests/test_utils.rs39
17 files changed, 34 insertions, 88 deletions
diff --git a/candle-core/src/lib.rs b/candle-core/src/lib.rs
index ddd446ee..fa85f6e0 100644
--- a/candle-core/src/lib.rs
+++ b/candle-core/src/lib.rs
@@ -63,6 +63,7 @@ pub mod shape;
mod storage;
mod strided_index;
mod tensor;
+pub mod test_utils;
pub mod utils;
mod variable;
diff --git a/candle-core/tests/test_utils.rs b/candle-core/src/test_utils.rs
index 327e88c6..8ff73fc0 100644
--- a/candle-core/tests/test_utils.rs
+++ b/candle-core/src/test_utils.rs
@@ -1,9 +1,4 @@
-#![allow(dead_code)]
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use candle_core::{Result, Tensor};
+use crate::{Result, Tensor};
#[macro_export]
macro_rules! test_device {
@@ -23,6 +18,12 @@ macro_rules! test_device {
};
}
+pub fn to_vec0_round(t: &Tensor, digits: i32) -> Result<f32> {
+ let b = 10f32.powi(digits);
+ let t = t.to_vec0::<f32>()?;
+ Ok(f32::round(t * b) / b)
+}
+
pub fn to_vec1_round(t: &Tensor, digits: i32) -> Result<Vec<f32>> {
let b = 10f32.powi(digits);
let t = t.to_vec1::<f32>()?;
@@ -40,7 +41,7 @@ pub fn to_vec2_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
Ok(t)
}
-pub fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
+pub fn to_vec3_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
let b = 10f32.powi(digits);
let t = t.to_vec3::<f32>()?;
let t = t
diff --git a/candle-core/tests/conv_tests.rs b/candle-core/tests/conv_tests.rs
index 7a4c2956..2fdd4c74 100644
--- a/candle-core/tests/conv_tests.rs
+++ b/candle-core/tests/conv_tests.rs
@@ -1,6 +1,5 @@
-mod test_utils;
use anyhow::Result;
-use candle_core::{Device, Tensor};
+use candle_core::{test_device, test_utils, Device, Tensor};
/* This test is based on the following script.
import torch
diff --git a/candle-core/tests/custom_op_tests.rs b/candle-core/tests/custom_op_tests.rs
index 7ec04c6a..cff0aebe 100644
--- a/candle-core/tests/custom_op_tests.rs
+++ b/candle-core/tests/custom_op_tests.rs
@@ -1,10 +1,8 @@
use candle_core::backend::BackendStorage;
use candle_core::cpu_backend;
+use candle_core::test_utils::to_vec1_round;
use candle_core::{CpuStorage, CustomOp1, DType, Device, Error, Layout, Result, Shape, Tensor};
-mod test_utils;
-use test_utils::to_vec1_round;
-
fn fwd<T: num_traits::Float>(v: T, alpha: f64) -> T {
if v.is_sign_positive() {
v
diff --git a/candle-core/tests/grad_tests.rs b/candle-core/tests/grad_tests.rs
index c44a7ea7..81b1338a 100644
--- a/candle-core/tests/grad_tests.rs
+++ b/candle-core/tests/grad_tests.rs
@@ -1,6 +1,5 @@
use anyhow::{Context, Result};
-use candle_core::{Device, Shape, Tensor, Var};
-mod test_utils;
+use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
fn simple_grad(device: &Device) -> Result<()> {
let x = Var::new(&[3f32, 1., 4.], device)?;
diff --git a/candle-core/tests/indexing_tests.rs b/candle-core/tests/indexing_tests.rs
index 1a77b32d..9c88f319 100644
--- a/candle-core/tests/indexing_tests.rs
+++ b/candle-core/tests/indexing_tests.rs
@@ -1,8 +1,6 @@
use anyhow::Result;
use candle_core::{Device, IndexOp, Tensor};
-mod test_utils;
-
#[test]
fn integer_index() -> Result<()> {
let dev = Device::Cpu;
diff --git a/candle-core/tests/layout_tests.rs b/candle-core/tests/layout_tests.rs
index d7b89605..1b29476f 100644
--- a/candle-core/tests/layout_tests.rs
+++ b/candle-core/tests/layout_tests.rs
@@ -1,5 +1,4 @@
-mod test_utils;
-use candle::{Device, IndexOp, Result, Tensor};
+use candle::{test_device, Device, IndexOp, Result, Tensor};
use candle_core as candle;
fn contiguous(device: &Device) -> Result<()> {
diff --git a/candle-core/tests/pool_tests.rs b/candle-core/tests/pool_tests.rs
index bfdbc40d..b8c007b8 100644
--- a/candle-core/tests/pool_tests.rs
+++ b/candle-core/tests/pool_tests.rs
@@ -1,5 +1,4 @@
-mod test_utils;
-use candle_core::{Device, IndexOp, Result, Tensor};
+use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
@@ -56,14 +55,17 @@ fn avg_pool2d_pytorch(dev: &Device) -> Result<()> {
.reshape((1, 2, 4, 4))?;
let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?;
assert_eq!(
- test_utils::to_vec3_round(pool, 4)?,
+ test_utils::to_vec3_round(&pool, 4)?,
[
[[-1.1926, -0.0395], [0.2688, 0.1871]],
[[0.1835, -0.1606], [0.6249, 0.3217]]
]
);
let pool = t.avg_pool2d((3, 3), (3, 3))?.squeeze(0)?;
- assert_eq!(test_utils::to_vec3_round(pool, 4)?, [[[0.085]], [[0.0078]]]);
+ assert_eq!(
+ test_utils::to_vec3_round(&pool, 4)?,
+ [[[0.085]], [[0.0078]]]
+ );
let t = t.reshape((1, 1, 4, 8))?;
let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
diff --git a/candle-core/tests/quantized_tests.rs b/candle-core/tests/quantized_tests.rs
index d178b89b..4fd5e90c 100644
--- a/candle-core/tests/quantized_tests.rs
+++ b/candle-core/tests/quantized_tests.rs
@@ -1,11 +1,10 @@
use candle_core::{
quantized::{self, GgmlDType},
+ test_utils::to_vec2_round,
Device, Result, Tensor,
};
use quantized::{k_quants, GgmlType};
-mod test_utils;
use rand::prelude::*;
-use test_utils::to_vec2_round;
const GGML_TEST_SIZE: usize = 32 * 128;
diff --git a/candle-core/tests/tensor_tests.rs b/candle-core/tests/tensor_tests.rs
index 1038efdd..6af43196 100644
--- a/candle-core/tests/tensor_tests.rs
+++ b/candle-core/tests/tensor_tests.rs
@@ -1,5 +1,4 @@
-mod test_utils;
-use candle_core::{DType, Device, IndexOp, Result, Tensor};
+use candle_core::{test_device, DType, Device, IndexOp, Result, Tensor};
fn zeros(device: &Device) -> Result<()> {
let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
diff --git a/candle-nn/tests/batch_norm.rs b/candle-nn/tests/batch_norm.rs
index 7a3cfc18..209fc10a 100644
--- a/candle-nn/tests/batch_norm.rs
+++ b/candle-nn/tests/batch_norm.rs
@@ -4,10 +4,8 @@ extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
-mod test_utils;
-
use anyhow::Result;
-use candle::{DType, Device, Tensor};
+use candle::{test_utils, DType, Device, Tensor};
use candle_nn::BatchNorm;
/* The test below has been generated using the following PyTorch code:
diff --git a/candle-nn/tests/group_norm.rs b/candle-nn/tests/group_norm.rs
index eff66d17..8145a220 100644
--- a/candle-nn/tests/group_norm.rs
+++ b/candle-nn/tests/group_norm.rs
@@ -25,10 +25,9 @@ extern crate intel_mkl_src;
extern crate accelerate_src;
use anyhow::Result;
+use candle::test_utils::to_vec3_round;
use candle::{Device, Tensor};
use candle_nn::{GroupNorm, Module};
-mod test_utils;
-use test_utils::to_vec3_round;
#[test]
fn group_norm() -> Result<()> {
@@ -60,7 +59,7 @@ fn group_norm() -> Result<()> {
device,
)?;
assert_eq!(
- to_vec3_round(gn2.forward(&input)?, 4)?,
+ to_vec3_round(&gn2.forward(&input)?, 4)?,
&[
[
[-0.1653, 0.3748, -0.7866],
@@ -81,7 +80,7 @@ fn group_norm() -> Result<()> {
]
);
assert_eq!(
- to_vec3_round(gn3.forward(&input)?, 4)?,
+ to_vec3_round(&gn3.forward(&input)?, 4)?,
&[
[
[0.4560, 1.4014, -0.6313],
diff --git a/candle-nn/tests/layer_norm.rs b/candle-nn/tests/layer_norm.rs
index 0f43d804..f81c29bd 100644
--- a/candle-nn/tests/layer_norm.rs
+++ b/candle-nn/tests/layer_norm.rs
@@ -5,11 +5,9 @@ extern crate intel_mkl_src;
extern crate accelerate_src;
use anyhow::Result;
-use candle::{Device, Tensor};
+use candle::{test_utils, Device, Tensor};
use candle_nn::{LayerNorm, Module};
-mod test_utils;
-
#[test]
fn layer_norm() -> Result<()> {
let device = &Device::Cpu;
@@ -28,7 +26,7 @@ fn layer_norm() -> Result<()> {
let inp = Tensor::new(&[[[1f32, 2., 3.], [4., 5., 6.], [9., 8., 7.]]], device)?;
let res = ln.forward(&inp)?;
assert_eq!(
- test_utils::to_vec3_round(res.clone(), 4)?,
+ test_utils::to_vec3_round(&res, 4)?,
[[
[-3.1742, 0.5, 4.1742],
[-3.1742, 0.5, 4.1742],
@@ -41,7 +39,7 @@ fn layer_norm() -> Result<()> {
let std = (res.broadcast_sub(&mean)?.sqr()?.sum_keepdim(2)?.sqrt()? / 3.0)?;
// The standard deviation should be sqrt(`w`).
assert_eq!(
- test_utils::to_vec3_round(std, 4)?,
+ test_utils::to_vec3_round(&std, 4)?,
[[[1.7321], [1.7321], [1.7321]]]
);
Ok(())
diff --git a/candle-nn/tests/loss.rs b/candle-nn/tests/loss.rs
index c075c7fb..d772f176 100644
--- a/candle-nn/tests/loss.rs
+++ b/candle-nn/tests/loss.rs
@@ -4,9 +4,8 @@ extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
+use candle::test_utils::to_vec0_round;
use candle::{Device, Result, Tensor};
-mod test_utils;
-use test_utils::to_vec0_round;
/* Equivalent python code:
import torch
diff --git a/candle-nn/tests/ops.rs b/candle-nn/tests/ops.rs
index fcf39fd8..4ba8cfcc 100644
--- a/candle-nn/tests/ops.rs
+++ b/candle-nn/tests/ops.rs
@@ -4,10 +4,7 @@ extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
-mod test_utils;
-use test_utils::to_vec3_round;
-
-use candle::{Device, Result, Tensor};
+use candle::{test_utils::to_vec3_round, Device, Result, Tensor};
#[test]
fn softmax() -> Result<()> {
@@ -18,7 +15,7 @@ fn softmax() -> Result<()> {
let t1 = candle_nn::ops::softmax(&tensor.log()?, 1)?;
let t2 = candle_nn::ops::softmax(&tensor.log()?, 2)?;
assert_eq!(
- to_vec3_round(t0, 4)?,
+ to_vec3_round(&t0, 4)?,
&[
// 3/5, 1/2, 4/11
[[0.6, 0.5, 0.3636], [0.1111, 0.7143, 0.5294]],
@@ -27,7 +24,7 @@ fn softmax() -> Result<()> {
]
);
assert_eq!(
- to_vec3_round(t1, 4)?,
+ to_vec3_round(&t1, 4)?,
&[
// 3/4, 1/6, 4/13
[[0.75, 0.1667, 0.3077], [0.25, 0.8333, 0.6923]],
@@ -36,7 +33,7 @@ fn softmax() -> Result<()> {
]
);
assert_eq!(
- to_vec3_round(t2, 4)?,
+ to_vec3_round(&t2, 4)?,
&[
// (3, 1, 4) / 8, (1, 5, 9) / 15
[[0.375, 0.125, 0.5], [0.0667, 0.3333, 0.6]],
diff --git a/candle-nn/tests/optim.rs b/candle-nn/tests/optim.rs
index f1d3b3f5..673d0455 100644
--- a/candle-nn/tests/optim.rs
+++ b/candle-nn/tests/optim.rs
@@ -4,8 +4,7 @@ extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
-mod test_utils;
-use test_utils::{to_vec0_round, to_vec2_round};
+use candle::test_utils::{to_vec0_round, to_vec2_round};
use anyhow::Result;
use candle::{Device, Tensor, Var};
diff --git a/candle-nn/tests/test_utils.rs b/candle-nn/tests/test_utils.rs
deleted file mode 100644
index bb422cd9..00000000
--- a/candle-nn/tests/test_utils.rs
+++ /dev/null
@@ -1,39 +0,0 @@
-#![allow(dead_code)]
-use candle::{Result, Tensor};
-
-pub fn to_vec0_round(t: &Tensor, digits: i32) -> Result<f32> {
- let b = 10f32.powi(digits);
- let t = t.to_vec0::<f32>()?;
- Ok(f32::round(t * b) / b)
-}
-
-pub fn to_vec1_round(t: &Tensor, digits: i32) -> Result<Vec<f32>> {
- let b = 10f32.powi(digits);
- let t = t.to_vec1::<f32>()?;
- let t = t.iter().map(|t| f32::round(t * b) / b).collect();
- Ok(t)
-}
-
-pub fn to_vec2_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
- let b = 10f32.powi(digits);
- let t = t.to_vec2::<f32>()?;
- let t = t
- .iter()
- .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
- .collect();
- Ok(t)
-}
-
-pub fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
- let b = 10f32.powi(digits);
- let t = t.to_vec3::<f32>()?;
- let t = t
- .iter()
- .map(|t| {
- t.iter()
- .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
- .collect()
- })
- .collect();
- Ok(t)
-}
generated by cgit v1.2.3 (git 2.39.1) at 2025-08-16 04:07:05 +0000