Ben_Kosytorz
3a67c0979c
- 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
90 lines
3.2 KiB
Rust
90 lines
3.2 KiB
Rust
use super::*;
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use burn_tensor::{TensorData, Tolerance};
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#[test]
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fn should_diff_cumsum_dim0() {
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let data_1 = TensorData::from([[1.0, 7.0], [-2.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, -7.0], [2.0, 3.0]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.cumsum(0);
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let tensor_5 = tensor_1.clone().mul(tensor_4);
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let grads = tensor_5.sum().backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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// Expected gradients computed with PyTorch
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let expected = TensorData::from([[-14.0, 24.0], [17.0, 6.0]]);
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grad_1
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let expected = TensorData::from([[3.0, 10.0], [-1.0, 37.0]]);
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grad_2
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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}
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#[test]
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fn should_diff_cumsum_dim1() {
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let data_1 = TensorData::from([[1.0, 7.0], [-2.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, -7.0], [2.0, 3.0]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.cumsum(1);
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let tensor_5 = tensor_1.clone().mul(tensor_4);
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let grads = tensor_5.sum().backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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// Expected gradients computed with PyTorch
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let expected = TensorData::from([[1.0, 69.0], [-13.0, -28.0]]);
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grad_1
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let expected = TensorData::from([[18.0, 13.0], [71.0, 58.0]]);
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grad_2
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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}
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#[test]
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fn should_diff_cumsum_complex() {
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let data_1 = TensorData::from([[0.0, 1.0], [3.0, 4.0]]);
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let data_2 = TensorData::from([[6.0, 7.0], [9.0, 10.0]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.clone().cumsum(1);
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let tensor_5 = tensor_4.mul(tensor_3);
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let grads = tensor_5.sum().backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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// Expected gradients computed with PyTorch
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let expected = TensorData::from([[371.0, 542.0], [2246.0, 3281.0]]);
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grad_1
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let expected = TensorData::from([[507.0, 528.0], [704.0, 733.0]]);
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grad_2
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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}
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