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
104 lines
3.7 KiB
Rust
104 lines
3.7 KiB
Rust
use super::*;
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use burn_tensor::{TensorData, Tolerance};
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#[cfg(feature = "std")]
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use burn_backend_tests::might_panic;
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#[test]
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fn backward_basic() {
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let device = Default::default();
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let a = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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&device,
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)
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.require_grad();
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let b = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]),
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&device,
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)
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.require_grad();
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// Simple cross product; grad is a vector of ones.
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let c = a.clone().cross(b.clone(), 1);
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let grads = c.backward();
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let a_grad = a.grad(&grads).unwrap().to_data();
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let b_grad = b.grad(&grads).unwrap().to_data();
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// For a: b×grad_out, where grad_out = [1,1,1]
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let expected_a = TensorData::from([[-1.0, 2.0, -1.0], [-1.0, 2.0, -1.0]]);
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// For b: grad_out×a
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let expected_b = TensorData::from([[1.0, -2.0, 1.0], [1.0, -2.0, 1.0]]);
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a_grad.assert_approx_eq::<FloatElem>(&expected_a, Tolerance::default());
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b_grad.assert_approx_eq::<FloatElem>(&expected_b, Tolerance::default());
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}
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#[test]
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fn backward_after_sum() {
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let device = Default::default();
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let a = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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&device,
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)
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.require_grad();
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let b = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]),
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&device,
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)
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.require_grad();
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// Sum reduces to scalar, but the gradient should be the same.
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let c = a.clone().cross(b.clone(), 1).sum();
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let grads = c.backward();
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let a_grad = a.grad(&grads).unwrap().to_data();
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let b_grad = b.grad(&grads).unwrap().to_data();
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let expected_a = TensorData::from([[-1.0, 2.0, -1.0], [-1.0, 2.0, -1.0]]);
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let expected_b = TensorData::from([[1.0, -2.0, 1.0], [1.0, -2.0, 1.0]]);
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a_grad.assert_approx_eq::<FloatElem>(&expected_a, Tolerance::default());
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b_grad.assert_approx_eq::<FloatElem>(&expected_b, Tolerance::default());
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}
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#[cfg(feature = "std")]
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#[might_panic(reason = "not implemented: Cross product on non-last dimension")]
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#[test]
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fn different_dim() {
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// Also check when the cross is along a different dimension (e.g. dim 0).
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let device = Default::default();
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let a_raw = [[1.0, 4.0, 7.0], [2.0, 5.0, 8.0], [3.0, 6.0, 9.0]];
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let b_raw = [[9.0, 6.0, 3.0], [8.0, 5.0, 2.0], [7.0, 4.0, 1.0]];
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let a = TestTensor::<2>::from_data(TensorData::from(a_raw), &device);
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let b = TestTensor::<2>::from_data(TensorData::from(b_raw), &device);
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// Cross along dim 0. Some backends (for example CubeCL) may not support
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// cross on non-last dimensions and will intentionally panic with a
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// message like "Cross product on non-last dimension not yet implemented".
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// In that case we treat the panic as a skipped test for that backend.
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let out = a.cross(b.clone(), 0);
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// Manually compute cross of each column vector using raw arrays
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let expected = [
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[
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a_raw[1][0] * b_raw[2][0] - a_raw[2][0] * b_raw[1][0],
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a_raw[1][1] * b_raw[2][1] - a_raw[2][1] * b_raw[1][1],
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a_raw[1][2] * b_raw[2][2] - a_raw[2][2] * b_raw[1][2],
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],
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[
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a_raw[2][0] * b_raw[0][0] - a_raw[0][0] * b_raw[2][0],
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a_raw[2][1] * b_raw[0][1] - a_raw[0][1] * b_raw[2][1],
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a_raw[2][2] * b_raw[0][2] - a_raw[0][2] * b_raw[2][2],
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],
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[
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a_raw[0][0] * b_raw[1][0] - a_raw[1][0] * b_raw[0][0],
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a_raw[0][1] * b_raw[1][1] - a_raw[1][1] * b_raw[0][1],
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a_raw[0][2] * b_raw[1][2] - a_raw[1][2] * b_raw[0][2],
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],
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];
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out.to_data()
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.assert_approx_eq::<FloatElem>(&TensorData::from(expected), Tolerance::default());
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}
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