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Add the cpu implementation for reduce_sum.
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@ -171,13 +171,52 @@ impl CpuStorage {
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}
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}
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pub(crate) fn sum(
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&self,
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_shape: &Shape,
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_stride: &[usize],
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_sum_dims: &[usize],
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) -> Result<Self> {
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todo!()
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pub(crate) fn sum(&self, shape: &Shape, stride: &[usize], sum_dims: &[usize]) -> Result<Self> {
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let src_dims = shape.dims();
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let mut dst_dims = src_dims.to_vec();
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for &sum_dim in sum_dims.iter() {
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dst_dims[sum_dim] = 1;
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}
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let dst_shape = Shape::from(dst_dims);
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let sum_dims_and_stride: Vec<_> = src_dims
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.iter()
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.enumerate()
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.map(|(i, d)| (d, src_dims[i + 1..].iter().product::<usize>()))
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.collect();
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let to_dst_index = |unstr_index: usize| {
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// TODO: Optimize, the following does lots of slow division and modulos.
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let mut dst_index = unstr_index;
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// Set the sum_dims indexes to 0.
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for &(dim, stride) in sum_dims_and_stride.iter() {
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let index = dst_index / stride % dim;
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dst_index -= index * stride;
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}
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dst_index
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};
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// TODO: Maybe provide an implementation with higher precision accumulators?
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match self {
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Self::F32(src) => {
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let mut dst = vec![0f32; dst_shape.elem_count()];
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for (unstr_index, src_index) in StridedIndex::new(src_dims, stride).enumerate() {
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dst[to_dst_index(unstr_index)] += src[src_index];
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}
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Ok(Self::F32(dst))
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}
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Self::F64(src) => {
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let mut dst = vec![0f64; dst_shape.elem_count()];
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for (unstr_index, src_index) in StridedIndex::new(src_dims, stride).enumerate() {
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dst[to_dst_index(unstr_index)] += src[src_index];
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}
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Ok(Self::F64(dst))
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}
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Self::U32(src) => {
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let mut dst = vec![0u32; dst_shape.elem_count()];
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for (unstr_index, src_index) in StridedIndex::new(src_dims, stride).enumerate() {
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dst[to_dst_index(unstr_index)] += src[src_index];
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}
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Ok(Self::U32(dst))
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}
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}
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}
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pub(crate) fn divide_by_sum_over_dim(&mut self, shape: &Shape, dim: usize) -> Result<()> {
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