burn-tensor 0.18.0

Tensor library with user-friendly APIs and automatic differentiation support
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#[burn_tensor_testgen::testgen(q_clamp)]
mod tests {
    use super::*;
    use burn_tensor::TensorData;
    use burn_tensor::{Tolerance, ops::FloatElem};
    type FT = FloatElem<TestBackend>;

    #[test]
    fn clamp_min() {
        let tensor = QTensor::<TestBackend, 2>::int8([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);

        let output = tensor.clamp_min(2.0);

        // Precision 1 to approximate de/quantization errors
        output.dequantize().into_data().assert_approx_eq::<FT>(
            &TensorData::from([[2.0, 2.0, 2.0], [3.0, 4.0, 5.0]]),
            Tolerance::absolute(1e-1),
        );
    }

    #[test]
    fn clamp_max() {
        let tensor = QTensor::<TestBackend, 2>::int8([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);

        let output = tensor.clamp_max(2.0);

        // Precision 1 to approximate de/quantization errors
        output.dequantize().into_data().assert_approx_eq::<FT>(
            &TensorData::from([[0.0, 1.0, 2.0], [2.0, 2.0, 2.0]]),
            Tolerance::absolute(1e-1),
        );
    }

    #[test]
    fn clamp_min_max() {
        let tensor = QTensor::<TestBackend, 2>::int8([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);

        let output = tensor.clamp(1.0, 4.0);

        // Precision 1 to approximate de/quantization errors
        output.dequantize().into_data().assert_approx_eq::<FT>(
            &TensorData::from([[1.0, 1.0, 2.0], [3.0, 4.0, 4.0]]),
            Tolerance::absolute(1e-1),
        );
    }
}