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
118 lines
3.8 KiB
Rust
118 lines
3.8 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_cummax() {
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// Simple test to verify cummax gradients work
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let device = Default::default();
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let tensor = TestAutodiffTensor::<1>::from_data(TensorData::from([1.0, 3.0, 2.0]), &device)
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.require_grad();
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let output = tensor.clone().cummax(0);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// PyTorch reference: [1.0, 2.0, 0.0]
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let expected = TensorData::from([1.0, 2.0, 0.0]);
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grad.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_cummax_2d() {
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// Test 2D cummax gradients
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let device = Default::default();
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let tensor = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[1.0, 3.0, 2.0], [2.0, 5.0, 4.0]]),
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&device,
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)
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.require_grad();
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let output = tensor.clone().cummax(1);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// PyTorch reference: [[1.0, 2.0, 0.0], [1.0, 2.0, 0.0]]
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let expected = TensorData::from([[1.0, 2.0, 0.0], [1.0, 2.0, 0.0]]);
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grad.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_cummax_duplicate_values() {
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// Test with duplicate maximum values - critical edge case
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let device = Default::default();
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let tensor =
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TestAutodiffTensor::<1>::from_data(TensorData::from([1.0, 3.0, 3.0, 2.0]), &device)
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.require_grad();
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let output = tensor.clone().cummax(0);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// input: [1.0, 3.0, 3.0, 2.0]
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// cummax: [1.0, 3.0, 3.0, 3.0]
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// PyTorch reference: [1.0, 1.0, 2.0, 0.0]
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// Position 2 gets grad from itself + position 3
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let expected = TensorData::from([1.0, 1.0, 2.0, 0.0]);
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grad.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_cummax_all_same() {
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// Test with all same values
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let device = Default::default();
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let tensor = TestAutodiffTensor::<1>::from_data(TensorData::from([2.0, 2.0, 2.0]), &device)
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.require_grad();
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let output = tensor.clone().cummax(0);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// PyTorch reference: [1.0, 1.0, 1.0]
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// Each position matches cummax, so each gets its own gradient
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let expected = TensorData::from([1.0, 1.0, 1.0]);
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grad.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_cummax_increasing() {
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// Test with increasing sequence
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let device = Default::default();
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let tensor =
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TestAutodiffTensor::<1>::from_data(TensorData::from([1.0, 2.0, 3.0, 4.0]), &device)
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.require_grad();
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let output = tensor.clone().cummax(0);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// PyTorch reference: [1.0, 1.0, 1.0, 1.0]
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// Each position is a new maximum
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let expected = TensorData::from([1.0, 1.0, 1.0, 1.0]);
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grad.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_cummax_2d_duplicates() {
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// Test 2D with duplicate values
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let device = Default::default();
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let tensor = TestAutodiffTensor::<2>::from_data(
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TensorData::from([[1.0, 3.0, 3.0, 2.0], [2.0, 5.0, 5.0, 4.0]]),
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&device,
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)
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.require_grad();
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let output = tensor.clone().cummax(1);
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let grads = output.sum().backward();
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let grad = tensor.grad(&grads).unwrap();
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// PyTorch reference: [[1.0, 1.0, 2.0, 0.0], [1.0, 1.0, 2.0, 0.0]]
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let expected = TensorData::from([[1.0, 1.0, 2.0, 0.0], [1.0, 1.0, 2.0, 0.0]]);
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grad.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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}
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