encoderfile 0.6.2

Distribute and run transformer encoders with a single file.
Documentation 
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use super::Tensor;
use mlua::prelude::*;

impl Tensor {
    #[tracing::instrument(skip_all)]
    pub fn sum_axis(&self, axis: isize) -> Result<Self, LuaError> {
        Ok(Self(self.0.sum_axis(self.axis1(axis)?)))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_sum_axis_columns() {
        use ndarray::{Array2, Axis};
        let tensor = Tensor(
            Array2::<f32>::from_shape_vec((2, 3), vec![1., 2., 3., 4., 5., 6.])
                .unwrap()
                .into_dyn(),
        );
        let result = tensor.sum_axis(2).unwrap();
        let expected = Tensor(ndarray::array![6., 15.].into_dyn());
        assert_eq!(result, expected);

        let expected = tensor.0.sum_axis(Axis(1));
        assert_eq!(result, Tensor(expected));
    }

    #[test]
    fn test_sum_axis_rows() {
        use ndarray::{Array2, Axis};
        let tensor = Tensor(
            Array2::<f32>::from_shape_vec((2, 3), vec![1., 2., 3., 4., 5., 6.])
                .unwrap()
                .into_dyn(),
        );
        let result = tensor.sum_axis(1).unwrap();
        let expected = Tensor(ndarray::array![5., 7., 9.].into_dyn());
        assert_eq!(result, expected);

        let expected = tensor.0.sum_axis(Axis(0));
        assert_eq!(result, Tensor(expected));
    }

    #[test]
    fn test_sum_axis_invalid() {
        use ndarray::Array2;
        let tensor = Tensor(
            Array2::<f32>::from_shape_vec((2, 3), vec![1., 2., 3., 4., 5., 6.])
                .unwrap()
                .into_dyn(),
        );
        let result = tensor.sum_axis(3); // invalid axis (too large)
        assert!(result.is_err());
    }

    #[test]
    fn test_sum_axis_with_lua_binding() {
        use crate::transforms::tensor::load_env;
        use ndarray::Array2;
        let lua = load_env();
        let tensor = Tensor(
            Array2::<f32>::from_shape_vec((2, 3), vec![1., 2., 3., 4., 5., 6.])
                .unwrap()
                .into_dyn(),
        );

        let func = lua
            .load("return function(x) return x:sum_axis(2) end")
            .eval::<LuaFunction>()
            .unwrap();

        let result: Tensor = func.call(tensor.clone()).unwrap();
        let expected = Tensor(ndarray::array![6., 15.].into_dyn());
        assert_eq!(result, expected);
    }
}