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Move the test-utils bits to a shared place. (#619)

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This commit is contained in:
Laurent Mazare
2023-08-27 09:42:22 +01:00
committed by GitHub
parent a8b39dd7b7
commit 5320aa6b7d
17 changed files with 34 additions and 88 deletions
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4
candle-nn/tests/batch_norm.rs
View File

@ -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:

7
candle-nn/tests/group_norm.rs
View File

@ -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],

8
candle-nn/tests/layer_norm.rs
View File

@ -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(())

3
candle-nn/tests/loss.rs
View File

@ -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

11
candle-nn/tests/ops.rs
View File

@ -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]],

3
candle-nn/tests/optim.rs
View File

@ -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};

39
candle-nn/tests/test_utils.rs
View File

@ -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)
}