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
84 lines
2.9 KiB
Rust
84 lines
2.9 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() {
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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 grads = tensor_3.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
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.to_data()
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.assert_eq(&TensorData::from([[3.0, 3.0], [10.0, 10.0]]), false);
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tensor_3
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.to_data()
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.assert_eq(&TensorData::from([[18.0, 28.0], [14.0, 23.0]]), false);
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}
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#[test]
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fn test_matmul_complex_1() {
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let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
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let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);
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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 = TestAutodiffTensor::from_data(data_3, &device).require_grad();
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let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_5 = tensor_4.matmul(tensor_3);
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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
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.to_data()
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.assert_eq(&TensorData::from([[44.0, 20.0], [44.0, 20.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[56.0, 56.0], [16.0, 16.0]]), false);
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}
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#[test]
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fn test_matmul_complex_2() {
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let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
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let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);
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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 = TestAutodiffTensor::from_data(data_3, &device).require_grad();
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let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_5 = tensor_4.matmul(tensor_3.clone());
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let tensor_6 = tensor_1.clone().matmul(tensor_5);
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let grads = tensor_6.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([[800.0, 792.0], [360.0, 592.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[264., 264.0], [344.0, 344.0]]), false);
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}
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