Files
RustyUI/crates/stable-diffusion-burn/burn-crates/burn-backend-tests/tests/autodiff/pow.rs
Ben_Kosytorz 3a67c0979c feat: update workspace paths and enhance gitignore
- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution
- Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory
- Added Cargo.lock to gitignore with appropriate comment
- Reorganized IDE files section in gitignore for better clarity
- Added newline at end of file for proper formatting
2026-03-05 19:39:14 +01:00

94 lines
3.1 KiB
Rust

use super::*;
use burn_tensor::TensorData;
use burn_tensor::Tolerance;
#[test]
fn should_diff_powf_scalar() {
let data_1 = TensorData::from([[0.0, 1.0], [3.0, 4.0]]);
let data_2 = TensorData::from([[6.0, 7.0], [9.0, 10.0]]);
let device = Default::default();
let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().powf_scalar(0.4));
let tensor_4 = tensor_3.matmul(tensor_2.clone());
let grads = tensor_4.backward();
let grad_1 = tensor_1.grad(&grads).unwrap();
let grad_2 = tensor_2.grad(&grads).unwrap();
let tolerance = Tolerance::default().set_half_precision_relative(2e-3);
let expected = TensorData::from([[68.0, 79.0328], [68.0, 79.0328]]);
grad_1
.to_data()
.assert_approx_eq::<FloatElem>(&expected, tolerance);
let expected = TensorData::from([[23.5081, 25.2779], [26.0502, 28.6383]]);
grad_2
.to_data()
.assert_approx_eq::<FloatElem>(&expected, tolerance);
}
#[test]
fn should_diff_powf() {
let device = Default::default();
let tensor_1 = TestAutodiffTensor::<1>::from_data([2.0, 7.0], &device).require_grad();
let tensor_2 = TestAutodiffTensor::from_data([4.0, 2.0], &device).require_grad();
let tensor_3 = tensor_1.clone().powf(tensor_2.clone());
let grads = tensor_3.backward();
let grad_1 = tensor_1.grad(&grads).unwrap();
let grad_2 = tensor_2.grad(&grads).unwrap();
let expected = TensorData::from([32.0, 14.0]);
grad_1
.into_data()
.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
let expected = TensorData::from([11.09035, 95.34960]);
grad_2
.into_data()
.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
let expected = TensorData::from([16.0, 49.0]);
tensor_3
.into_data()
.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}
#[test]
fn should_diff_powf_with_untracked_lhs() {
let device = Default::default();
let tensor_1 = TestAutodiffTensor::<1>::from_data([2.0, 7.0], &device);
let tensor_2 = TestAutodiffTensor::from_data([4.0, 2.0], &device).require_grad();
let tensor_3 = tensor_1.clone().powf(tensor_2.clone());
let grads = tensor_3.backward();
let grad_2 = tensor_2.grad(&grads).unwrap();
let expected = TensorData::from([11.09035, 95.34960]);
grad_2
.to_data()
.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}
#[test]
fn should_diff_powf_with_untracked_rhs() {
let device = Default::default();
let tensor_1 = TestAutodiffTensor::<1>::from_data([2.0, 7.0], &device).require_grad();
let tensor_2 = TestAutodiffTensor::from_data([4.0, 2.0], &device);
let tensor_3 = tensor_1.clone().powf(tensor_2.clone());
let grads = tensor_3.backward();
let grad_1 = tensor_1.grad(&grads).unwrap();
let expected = TensorData::from([32.0, 14.0]);
grad_1
.into_data()
.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}