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Remove Zero bound for transpose and impl SparsityPattern::transpose

This commit is contained in:
Andreas Longva 2021-01-19 14:15:19 +01:00
parent 3b1303d1e0
commit ef3477f411
6 changed files with 151 additions and 90 deletions
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4
nalgebra-sparse/src/convert/impl_std_ops.rs
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@ -107,7 +107,7 @@ impl<'a, T> From<&'a CscMatrix<T>> for DMatrix<T>
impl<'a, T> From<&'a CscMatrix<T>> for CsrMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
fn from(matrix: &'a CscMatrix<T>) -> Self {
convert_csc_csr(matrix)
@ -116,7 +116,7 @@ impl<'a, T> From<&'a CscMatrix<T>> for CsrMatrix<T>
impl<'a, T> From<&'a CsrMatrix<T>> for CscMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
fn from(matrix: &'a CsrMatrix<T>) -> Self {
convert_csr_csc(matrix)

99
nalgebra-sparse/src/convert/serial.rs
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@ -1,12 +1,15 @@
//! TODO
use crate::coo::CooMatrix;
use crate::csr::CsrMatrix;
use nalgebra::{DMatrix, Scalar, Matrix, Dim, ClosedAdd};
use nalgebra::storage::Storage;
use std::ops::Add;
use num_traits::Zero;
use std::ops::{Add};
use nalgebra::{ClosedAdd, Dim, DMatrix, Matrix, Scalar};
use nalgebra::storage::Storage;
use crate::coo::CooMatrix;
use crate::cs;
use crate::csc::CscMatrix;
use crate::csr::CsrMatrix;
/// TODO
pub fn convert_dense_coo<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CooMatrix<T>
@ -192,13 +195,13 @@ pub fn convert_dense_csc<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CscMatrix<T>
/// TODO
pub fn convert_csr_csc<T>(csr: &CsrMatrix<T>) -> CscMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
let (offsets, indices, values) = transpose_cs(csr.nrows(),
csr.ncols(),
csr.row_offsets(),
csr.col_indices(),
csr.values());
let (offsets, indices, values) = cs::transpose_cs(csr.nrows(),
csr.ncols(),
csr.row_offsets(),
csr.col_indices(),
csr.values());
// TODO: Avoid data validity check?
CscMatrix::try_from_csc_data(csr.nrows(), csr.ncols(), offsets, indices, values)
@ -208,13 +211,13 @@ where
/// TODO
pub fn convert_csc_csr<T>(csc: &CscMatrix<T>) -> CsrMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
let (offsets, indices, values) = transpose_cs(csc.ncols(),
csc.nrows(),
csc.col_offsets(),
csc.row_indices(),
csc.values());
let (offsets, indices, values) = cs::transpose_cs(csc.ncols(),
csc.nrows(),
csc.col_offsets(),
csc.row_indices(),
csc.values());
// TODO: Avoid data validity check?
CsrMatrix::try_from_csr_data(csc.nrows(), csc.ncols(), offsets, indices, values)
@ -326,7 +329,7 @@ fn coo_to_unsorted_cs<T: Clone>(
major_offsets[*major_idx] += 1;
}
convert_counts_to_offsets(major_offsets);
cs::convert_counts_to_offsets(major_offsets);
{
// TODO: Instead of allocating a whole new vector storing the current counts,
@ -377,66 +380,6 @@ fn sort_lane<T: Clone>(
apply_permutation(values_result, values, permutation);
}
/// Transposes the compressed format.
///
/// This means that major and minor roles are switched. This is used for converting between CSR
/// and CSC formats.
fn transpose_cs<T>(major_dim: usize,
minor_dim: usize,
source_major_offsets: &[usize],
source_minor_indices: &[usize],
values: &[T])
-> (Vec<usize>, Vec<usize>, Vec<T>)
where
T: Scalar + Zero
{
assert_eq!(source_major_offsets.len(), major_dim + 1);
assert_eq!(source_minor_indices.len(), values.len());
let nnz = values.len();
// Count the number of occurences of each minor index
let mut minor_counts = vec![0; minor_dim];
for minor_idx in source_minor_indices {
minor_counts[*minor_idx] += 1;
}
convert_counts_to_offsets(&mut minor_counts);
let mut target_offsets = minor_counts;
target_offsets.push(nnz);
let mut target_indices = vec![usize::MAX; nnz];
let mut target_values = vec![T::zero(); nnz];
// Keep track of how many entries we have placed in each target major lane
let mut current_target_major_counts = vec![0; minor_dim];
for source_major_idx in 0 .. major_dim {
let source_lane_begin = source_major_offsets[source_major_idx];
let source_lane_end = source_major_offsets[source_major_idx + 1];
let source_lane_indices = &source_minor_indices[source_lane_begin .. source_lane_end];
let source_lane_values = &values[source_lane_begin .. source_lane_end];
for (&source_minor_idx, val) in source_lane_indices.iter().zip(source_lane_values) {
// Compute the offset in the target data for this particular source entry
let target_lane_count = &mut current_target_major_counts[source_minor_idx];
let entry_offset = target_offsets[source_minor_idx] + *target_lane_count;
target_indices[entry_offset] = source_major_idx;
target_values[entry_offset] = val.inlined_clone();
*target_lane_count += 1;
}
}
(target_offsets, target_indices, target_values)
}
fn convert_counts_to_offsets(counts: &mut [usize]) {
// Convert the counts to an offset
let mut offset = 0;
for i_offset in counts.iter_mut() {
let count = *i_offset;
*i_offset = offset;
offset += count;
}
}
// TODO: Move this into `utils` or something?
fn apply_permutation<T: Clone>(out_slice: &mut [T], in_slice: &[T], permutation: &[usize]) {
assert_eq!(out_slice.len(), in_slice.len());

109
nalgebra-sparse/src/cs.rs
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@ -1,12 +1,14 @@
use crate::pattern::SparsityPattern;
use crate::{SparseEntry, SparseEntryMut};
use std::sync::Arc;
use std::ops::Range;
use std::mem::replace;
use std::ops::Range;
use std::sync::Arc;
use num_traits::One;
use nalgebra::Scalar;
use crate::{SparseEntry, SparseEntryMut};
use crate::pattern::SparsityPattern;
/// An abstract compressed matrix.
///
/// For the time being, this is only used internally to share implementation between
@ -397,3 +399,100 @@ impl<'a, T> CsLaneMut<'a, T> {
global_minor_index)
}
}
/// Helper struct for working with uninitialized data in vectors.
/// TODO: This doesn't belong here.
struct UninitVec<T> {
vec: Vec<T>
}
impl<T> UninitVec<T> {
pub fn from_len(len: usize) -> Self {
Self {
vec: Vec::with_capacity(len)
}
}
/// Sets the element associated with the given index to the provided value.
///
/// Must be called exactly once per index, otherwise results in undefined behavior.
pub unsafe fn set(&mut self, index: usize, value: T) {
self.vec.as_mut_ptr().add(index).write(value)
}
/// Marks the vector data as initialized by returning a full vector.
///
/// It is undefined behavior to call this function unless *all* elements have been written to
/// exactly once.
pub unsafe fn assume_init(mut self) -> Vec<T> {
self.vec.set_len(self.vec.capacity());
self.vec
}
}
/// Transposes the compressed format.
///
/// This means that major and minor roles are switched. This is used for converting between CSR
/// and CSC formats.
pub fn transpose_cs<T>(
major_dim: usize,
minor_dim: usize,
source_major_offsets: &[usize],
source_minor_indices: &[usize],
values: &[T])
-> (Vec<usize>, Vec<usize>, Vec<T>)
where
T: Scalar
{
assert_eq!(source_major_offsets.len(), major_dim + 1);
assert_eq!(source_minor_indices.len(), values.len());
let nnz = values.len();
// Count the number of occurences of each minor index
let mut minor_counts = vec![0; minor_dim];
for minor_idx in source_minor_indices {
minor_counts[*minor_idx] += 1;
}
convert_counts_to_offsets(&mut minor_counts);
let mut target_offsets = minor_counts;
target_offsets.push(nnz);
let mut target_indices = vec![usize::MAX; nnz];
// We have to use uninitialized storage, because we don't have any kind of "default" value
// available for `T`. Unfortunately this necessitates some small amount of unsafe code
let mut target_values = UninitVec::from_len(nnz);
// Keep track of how many entries we have placed in each target major lane
let mut current_target_major_counts = vec![0; minor_dim];
for source_major_idx in 0 .. major_dim {
let source_lane_begin = source_major_offsets[source_major_idx];
let source_lane_end = source_major_offsets[source_major_idx + 1];
let source_lane_indices = &source_minor_indices[source_lane_begin .. source_lane_end];
let source_lane_values = &values[source_lane_begin .. source_lane_end];
for (&source_minor_idx, val) in source_lane_indices.iter().zip(source_lane_values) {
// Compute the offset in the target data for this particular source entry
let target_lane_count = &mut current_target_major_counts[source_minor_idx];
let entry_offset = target_offsets[source_minor_idx] + *target_lane_count;
target_indices[entry_offset] = source_major_idx;
unsafe { target_values.set(entry_offset, val.inlined_clone()); }
*target_lane_count += 1;
}
}
// At this point, we should have written to each element in target_values exactly once,
// so initialization should be sound
let target_values = unsafe { target_values.assume_init() };
(target_offsets, target_indices, target_values)
}
pub fn convert_counts_to_offsets(counts: &mut [usize]) {
// Convert the counts to an offset
let mut offset = 0;
for i_offset in counts.iter_mut() {
let count = *i_offset;
*i_offset = offset;
offset += count;
}
}

4
nalgebra-sparse/src/csc.rs
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@ -7,7 +7,7 @@ use crate::cs::{CsMatrix, CsLane, CsLaneMut, CsLaneIter, CsLaneIterMut};
use std::sync::Arc;
use std::slice::{IterMut, Iter};
use num_traits::{Zero, One};
use num_traits::{One};
use nalgebra::Scalar;
/// A CSC representation of a sparse matrix.
@ -368,7 +368,7 @@ impl<T> CscMatrix<T> {
impl<T> CscMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
/// Compute the transpose of the matrix.
pub fn transpose(&self) -> CscMatrix<T> {

4
nalgebra-sparse/src/csr.rs
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@ -5,7 +5,7 @@ use crate::csc::CscMatrix;
use crate::cs::{CsMatrix, CsLaneIterMut, CsLaneIter, CsLane, CsLaneMut};
use nalgebra::Scalar;
use num_traits::{Zero, One};
use num_traits::{One};
use std::sync::Arc;
use std::slice::{IterMut, Iter};
@ -368,7 +368,7 @@ impl<T> CsrMatrix<T> {
impl<T> CsrMatrix<T>
where
T: Scalar + Zero
T: Scalar
{
/// Compute the transpose of the matrix.
pub fn transpose(&self) -> CsrMatrix<T> {

19
nalgebra-sparse/src/pattern.rs
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@ -2,6 +2,7 @@
use crate::SparseFormatError;
use std::fmt;
use std::error::Error;
use crate::cs::transpose_cs;
/// A representation of the sparsity pattern of a CSR or CSC matrix.
///
@ -204,6 +205,24 @@ impl SparsityPattern {
pub fn disassemble(self) -> (Vec<usize>, Vec<usize>) {
(self.major_offsets, self.minor_indices)
}
/// TODO
pub fn transpose(&self) -> Self {
// By using unit () values, we can use the same routines as for CSR/CSC matrices
let values = vec![(); self.nnz()];
let (new_offsets, new_indices, _) = transpose_cs(
self.major_dim(),
self.minor_dim(),
self.major_offsets(),
self.minor_indices(),
&values);
// TODO: Skip checks
Self::try_from_offsets_and_indices(self.minor_dim(),
self.major_dim(),
new_offsets,
new_indices)
.expect("Internal error: Transpose should never fail.")
}
}
/// Error type for `SparsityPattern` format errors.