ruDNN 0.1.76

Neural network operators for Ruda.
Documentation 
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use super::bilinear::bilinear_coordinate;
use ruda_kernel::dsl as kernel_dsl;
use ruda_kernel::dsl::calculate_ruda_count_elemwise;
use ruda_kernel::dsl::num_traits::{One, Zero};
use ruda_kernel::dsl::prelude::*;
use ruda_kernel::library::FastDivmod;
use ruda_kernel::library::tensor::layout::linear::LinearLayout;
use ruda_kernel::library::tensor::layout::*;
use ruda_kernel::tensor::RudaTensor;
use ruda_kernel::tensor::layout::{address_type, linear_layout, max_vector_size_many, shape_divmod};

#[ruda]
fn contributing_range(
    input_index: usize,
    input_size: usize,
    output_size: usize,
    #[comptime] align_corners: bool,
) -> (usize, usize) {
    let mut left = 0usize;
    let mut right = output_size;
    while left < right {
        let middle = left + (right - left) / 2;
        let coordinate = bilinear_coordinate(middle, input_size, output_size, align_corners);
        let upper = coordinate.ceil() as usize;
        if upper < input_index {
            left = middle + 1;
        } else {
            right = middle;
        }
    }
    let start = left;
    right = output_size;
    while left < right {
        let middle = left + (right - left) / 2;
        let coordinate = bilinear_coordinate(middle, input_size, output_size, align_corners);
        let lower = coordinate.floor() as usize;
        if lower <= input_index {
            left = middle + 1;
        } else {
            right = middle;
        }
    }
    (start, left)
}

#[ruda(launch, address_type = "dynamic")]
fn interpolate_bilinear_backward_kernel<F: Float, G: Float, A: Float, N: Size>(
    grad: &Tensor<Vector<G, N>>,
    output: &mut Tensor<Vector<F, N>>,
    shape_out: Sequence<FastDivmod<usize>>,
    out_layout: LinearLayout,
    #[comptime] align_corners: bool,
    #[define(F, G, A)] _types: [StorageType; 3],
) {
    if ABSOLUTE_POS >= output.len() {
        terminate!();
    }

    let vector_size = grad.vector_size();
    let out_idx = out_layout.to_source_pos(ABSOLUTE_POS);
    let (rem, channel) = shape_out[3].div_mod(ABSOLUTE_POS * vector_size);
    let (rem, input_x) = shape_out[2].div_mod(rem);
    let (batch, input_y) = shape_out[1].div_mod(rem);
    let height = output.shape(1);
    let width = output.shape(2);
    let grad_height = grad.shape(1);
    let grad_width = grad.shape(2);
    let (y_start, y_end) = contributing_range(input_y, height, grad_height, align_corners);
    let (x_start, x_end) = contributing_range(input_x, width, grad_width, align_corners);
    let index_base = batch * grad.stride(0) + channel * grad.stride(3);
    let mut sum = Vector::<A, N>::zero();

    for y in y_start..y_end {
        let coordinate = bilinear_coordinate(y, height, grad_height, align_corners);
        let lower = coordinate.floor();
        let y0 = lower as usize;
        let y1 = coordinate.ceil() as usize;
        let yw = F::cast_from(coordinate - lower);
        let yw_ = Vector::<A, N>::cast_from(F::one() - yw);
        let yw = Vector::<A, N>::cast_from(yw);

        for x in x_start..x_end {
            let coordinate = bilinear_coordinate(x, width, grad_width, align_corners);
            let lower = coordinate.floor();
            let x0 = lower as usize;
            let x1 = coordinate.ceil() as usize;
            let xw = F::cast_from(coordinate - lower);
            let xw_ = Vector::<A, N>::cast_from(F::one() - xw);
            let xw = Vector::<A, N>::cast_from(xw);
            let index = index_base + y * grad.stride(1) + x * grad.stride(2);
            let value = Vector::<A, N>::cast_from(grad[index / vector_size]);

            if input_y == y0 && input_x == x0 {
                sum += value * xw_ * yw_;
            }
            if input_y == y0 && input_x == x1 {
                sum += value * xw * yw_;
            }
            if input_y == y1 && input_x == x0 {
                sum += value * xw_ * yw;
            }
            if input_y == y1 && input_x == x1 {
                sum += value * xw * yw;
            }
        }
    }

    output[out_idx] = Vector::<F, N>::cast_from(sum);
}

pub fn interpolate_bilinear_backward_launch<R: Runtime>(
    out_grad: RudaTensor<R>,
    output: RudaTensor<R>,
    align_corners: bool,
) -> RudaTensor<R> {
    if output.meta.num_elements() == 0 {
        return output;
    }
    let vector_size = max_vector_size_many(&[&out_grad, &output], 3);
    let types = super::base::backward_types(output.dtype, out_grad.dtype);
    let out_shape = shape_divmod(&output);
    let out_layout = linear_layout(&output, vector_size);
    let working_units = output.meta.num_elements() / vector_size as usize;
    let ruda_dim = RudaDim::new(out_grad.client.properties(), working_units);
    let ruda_count = calculate_ruda_count_elemwise(&out_grad.client, working_units, ruda_dim);

    interpolate_bilinear_backward_kernel::launch(
        &output.client,
        ruda_count,
        ruda_dim,
        address_type!(out_grad, output),
        vector_size,
        out_grad.into_tensor_arg(),
        output.clone().into_tensor_arg(),
        out_shape,
        out_layout,
        align_corners,
        types,
    );

    output
}