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
68 lines
2.3 KiB
Rust
68 lines
2.3 KiB
Rust
use super::*;
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use burn_tensor::TensorData;
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#[test]
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fn should_diff_matmul_with_slice() {
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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, 100.0], [2.0, 3.0, 15.0]]);
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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_2.clone().slice([0..2, 0..2]);
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let tensor_4 = tensor_1.clone().matmul(tensor_3);
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let grads = tensor_4.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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grad_1
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.to_data()
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.assert_eq(&TensorData::from([[11.0, 5.0], [11.0, 5.0]]), false);
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grad_2.to_data().assert_eq(
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&TensorData::from([[3.0, 3.0, 0.0], [10.0, 10.0, 0.0]]),
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false,
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);
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}
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#[test]
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fn should_diff_matmul_with_slice_stepped() {
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use burn_tensor::s;
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let data_1 = TensorData::from([[1.0, 7.0], [100.0, 100.0], [2.0, 3.0], [100.0, 100.0]]);
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let data_2 = TensorData::from([[4.0, 100.0, 7.0, 100.0], [2.0, 100.0, 3.0, 15.0]]);
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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().slice(s![0..;2, 0..2]); // [[1., 7.], [2., 3.]]
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let tensor_4 = tensor_2.clone().slice(s![0..2, 0..;2]); // [[4., 7.], [2., 3.]]
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let tensor_5 = tensor_3.clone().matmul(tensor_4);
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let grads = tensor_5.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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grad_1.to_data().assert_eq(
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&TensorData::from([[11., 5.], [0., 0.], [11., 5.], [0., 0.]]),
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false,
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);
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grad_2.to_data().assert_eq(
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&TensorData::from([[3., 0., 3., 0.], [10., 0., 10., 0.]]),
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false,
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);
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}
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#[test]
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fn should_panic_on_slice_with_step() {
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use burn_tensor::s;
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let data = TensorData::from([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]);
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let device = Default::default();
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let tensor = TestAutodiffTensor::<2>::from_data(data, &device).require_grad();
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// This should panic because step is 2
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let _sliced = tensor.slice(s![.., 0..4; 2]);
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}
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