I want to perform a sum reduction along arbitrary axes of a multidimensional matrix which may have arbitrary dimensions (eg axis 5 of a 10-dimensional array). The matrix is stored using the row-major format, ie as a vector
together with the strides along each axis.
I know how to perform this reduction using nested loops (see example below), but doing this results in a hard-coded axis (the reduction is along axis 1 below) and an arbitrary number of dimensions (4 below). How can I generalize this without using the nested loops?
#include <iostream>
#include <vector>
int main()
{
// shape, stride & data of the matrix
size_t shape [] = { 2, 3, 4, 5};
size_t strides[] = {60,20, 5, 1};
std::vector<double> data(2*3*4*5);
for ( size_t i = 0 ; i < data.size() ; ++i ) data[i] = 1.;
// shape, stride & data (zero-initialized) of the reduced matrix
size_t rshape [] = { 2, 4, 5};
size_t rstrides[] = {20, 5, 1};
std::vector<double> rdata(2*4*5, 0.0);
// compute reduction
for ( size_t a = 0 ; a < shape[0] ; ++a )
for ( size_t c = 0 ; c < shape[2] ; ++c )
for ( size_t d = 0 ; d < shape[3] ; ++d )
for ( size_t b = 0 ; b < shape[1] ; ++b )
rdata[ a*rstrides[0] + c*rstrides[1] + d*rstrides[2] ] += \
data [ a*strides [0] + b*strides [1] + c*strides [2] + d*strides [3] ];
// print resulting reduced matrix
for ( size_t a = 0 ; a < rshape[0] ; ++a )
for ( size_t b = 0 ; b < rshape[1] ; ++b )
for ( size_t c = 0 ; c < rshape[2] ; ++c )
std::cout << "(" << a << "," << b << "," << c << ") " << \
rdata[ a*rstrides[0] + b*rstrides[1] + c*rstrides[2] ] << std::endl;
return 0;
}
Note: I want to avoid 'decompressing' and 'compressing' a counter. By this I mean that I could, in pseudo-code, do:
for ( size_t i = 0 ; i < data.size() ; ++i )
{
i -> {a,b,c,d}
discard "b" (axis 1) -> {a,c,d}
rdata(a,c,d) += data(a,b,c,d)
}
I don't know how efficient this code is, but in my opinion, it is sure to be precise.
A little on adjusted_strides
:
For axis_count = 4
, adjusted_strides
has size 5
, where:
adjusted_strides[0] = shape[0]*shape[1]*shape[2]*shape[3];
adjusted_strides[1] = shape[1]*shape[2]*shape[3];
adjusted_strides[2] = shape[2]*shape[3];
adjusted_strides[3] = shape[3];
adjusted_strides[4] = 1;
Let's take the example where the number of dimensions is 4
and the shape of the multidimensional array ( A
) is n0, n1, n2, n3
.
When we need to transform this array into another multidimensional array ( B
) of shape: n0, n2, n3
(compressing axis = 1 (0-based)
), then, we try to proceed as follows:
For each index of A
we try to find its position in B
. Let A[i][j][k][l]
be any element in A
. Its position in flat_A
will be A[i*n1*n2*n3 + j*n2*n3 + k*n3 + l]
idx = i*n1*n2*n3 + j*n2*n3 + k*n3 + l;
In the compressed array B
, this element will be a part of (or added to), B[i][k][l]
. In flat_B
the index is new_idx = i*n2*n3 + k*n3 + l;
.
How do we form new_idx
from idx
?
All the axes before the compressed axis have the shape of the compressed axis as a part of their product. In our example we had to remove axis 1
, so all the axes which were before the 1st axis (only one here: the 0th axis
) represented by i
), have n1
as a part of product ( i*n1*n2*n3
).
All the axes after the compressed axis remain unaffected.
Finally, we need to do two things:
Isolate the indices of the axes before the index of the axis to be compressed and remove the shape of this axis:
Integer division : idx / (n1*n2*n3);
( == idx / adjusted_strides[1]
).
We are left with just i
, which can be readjusted according to the new shape (by multiplying with n2*n3
): we get
i*n2*n3
( == i * adjusted_strides[2]
).
We isolate the axes after the compressed axis, which are unaffected by its shape.
idx % (n2*n3)
( == idx % adjusted_strides[2]
)
which gives us k*n3 + l
.
Adding the results of step i. and ii. results in:
computed_idx = i*n2*n3 + k*n3 + l;
Which is the same as new_idx
. So, our transformation was correct :).
Note: ni
refers to new_idx
.
size_t cmp_axis = 1, axis_count = sizeof shape/ sizeof *shape;
std::vector<size_t> adjusted_strides;
//adjusted strides is basically same as strides
//only difference being that the first element is the
//total number of elements in the n dim array.
//The only reason to introduce this array was
//so that I don't have to write any if-elses
adjusted_strides.push_back(shape[0]*strides[0]);
adjusted_strides.insert(adjusted_strides.end(), strides, strides + axis_count);
for(size_t i = 0; i < data.size(); ++i) {
size_t ni = i/adjusted_strides[cmp_axis]*adjusted_strides[cmp_axis+1] + i%adjusted_strides[cmp_axis+1];
rdata[ni] += data[i];
}
(0,0,0) 3
(0,0,1) 3
(0,0,2) 3
(0,0,3) 3
(0,0,4) 3
(0,1,0) 3
(0,1,1) 3
(0,1,2) 3
(0,1,3) 3
(0,1,4) 3
(0,2,0) 3
(0,2,1) 3
(0,2,2) 3
(0,2,3) 3
(0,2,4) 3
(0,3,0) 3
(0,3,1) 3
(0,3,2) 3
...
Tested here .
For further reading, refer to this .
I think this should work:
#include <iostream>
#include <vector>
int main()
{
// shape, stride & data of the matrix
size_t shape [] = { 2, 3, 4, 5};
size_t strides[] = {60, 20, 5, 1};
std::vector<double> data(2 * 3 * 4 * 5);
size_t rshape [] = { 2, 4, 5};
size_t rstrides[] = {3, 5, 1};
std::vector<double> rdata(2 * 4 * 5, 0.0);
const unsigned int NDIM = 4;
unsigned int axis = 1;
for (size_t i = 0 ; i < data.size() ; ++i) data[i] = 1;
// How many elements to advance after each reduction
size_t step_axis = strides[NDIM - 1];
if (axis == NDIM - 1)
{
step_axis = strides[NDIM - 2];
}
// Position of the first element of the current reduction
size_t offset_base = 0;
size_t offset = 0;
size_t s = 0;
for (auto &v : rdata)
{
// Current reduced element
size_t offset_i = offset;
for (unsigned int i = 0; i < shape[axis]; i++)
{
// Reduce
v += *(data.data() + offset_i);
// Advance to next element
offset_i += strides[axis];
}
s = (s + 1) % strides[axis];
if (s == 0)
{
offset_base += strides[axis - 1];
offset = offset_base;
}
else
{
offset += step_axis;
}
}
// Print
for ( size_t a = 0 ; a < rshape[0] ; ++a )
for ( size_t b = 0 ; b < rshape[1] ; ++b )
for ( size_t c = 0 ; c < rshape[2] ; ++c )
std::cout << "(" << a << "," << b << "," << c << ") " << \
rdata[ a*rstrides[0] + b*rstrides[1] + c*rstrides[2] ] << std::endl;
return 0;
}
Output:
(0,0,0) 3
(0,0,1) 3
(0,0,2) 3
(0,0,3) 3
(0,0,4) 3
(0,1,0) 3
(0,1,1) 3
(0,1,2) 3
(0,1,3) 3
(0,1,4) 3
(0,2,0) 3
(0,2,1) 3
(0,2,2) 3
// ...
Setting axis = 3
yields:
(0,0,0) 5
(0,0,1) 5
(0,0,2) 5
(0,0,3) 5
(0,0,4) 5
(0,1,0) 5
(0,1,1) 5
(0,1,2) 5
(0,1,3) 5
(0,1,4) 5
(0,2,0) 5
(0,2,1) 5
(0,2,2) 5
(0,2,3) 5
// ...
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