Not sure how to describe this/where to attribute blame. Also, this isn't a pressing issue as I think it's unlikely to arise outside of unit testing.
This issue concerns how ToTs are represented when an entire independent index is screened out. This is probably easiest to explain with an example.
Consider the following two ToTs as printed by TA. The first ToT:
0: [ [0,0], [1,2] ) {
[0,0]:[ [0], [2] ) { 1 2 }
[0,1]:[ [0], [2] ) { 7 8 }
}
2: [ [1,0], [2,2] ) {
[1,0]:[ [0], [2] ) { 0 2 }
[1,1]:[ [0], [2] ) { 6 8 }
}
7: [ [3,2], [4,3] ) {
[3,2]:[ [0], [2] ) { 12 13 }
}This tensor is made with make_array and a lambda. As you can see when TA hits the screened out independent indices (1, 3,4,5, and 6) it implicitly stores the tiles associated with those indices. Now consider another tensor:
0: [ [0,0], [1,2] ) {
[0,0]:[ [0], [2] ) { 1 2 }
[0,1]:[ [0], [2] ) { 7 8 }
}
1: [ [0,2], [1,3] ) {
[0,2]:[ [], [] ) { }
}
2: [ [1,0], [2,2] ) {
[1,0]:[ [0], [2] ) { 0 2 }
[1,1]:[ [0], [2] ) { 6 8 }
}
3: [ [1,2], [2,3] ) {
[1,2]:[ [], [] ) { }
}
4: [ [2,0], [3,2] ) {
[2,0]:[ [], [] ) { }
[2,1]:[ [], [] ) { }
}
5: [ [2,2], [3,3] ) {
[2,2]:[ [], [] ) { }
}
6: [ [3,0], [4,2] ) {
[3,0]:[ [], [] ) { }
[3,1]:[ [], [] ) { }
}
7: [ [3,2], [4,3] ) {
[3,2]:[ [0], [2] ) { 12 13 }
}This tensor is created with an initializer list like:
using inner_tile = TA::Tensor<double>;
inner_tile corr00{TA::Range{2},{1, 2}};
inner_tile corr01{TA::Range{2}, {7, 8}};
inner_tile corr10{TA::Range{2}, {0, 2}};
inner_tile corr11{TA::Range{2},{6, 8}};
inner_tile corr32{TA::Range{2}, {12, 13}};
inner_tile zero;
TA::detail::matrix_il<inner_tile> corr_il{
{corr00, corr01, zero},
{corr10, corr11, zero},
{zero, zero, zero},
{zero, zero, corr32}
};Note you have to put the zero place-holder inner tensors there as all elements of the outer tensors need to be initialized. Also note you can't do something like: inner_tile zero{TA::Range{1}, {0}} because that's a different shape (you're now saying the independent index maps to a single dependent index, whose value is zero as opposed to mapping to nothing). The intent is for these tensors to be the same, but attempting to subtract them gives an error:
TiledArray/tensor/kernels.h:422: void TiledArray::detail::tensor_init(Op&&, TR&, const Ts& ...) [with Op = TiledArray::Tensor< <template-parameter-1-1>, <template-parameter-1-2> >::neg() const [with T = TiledArray::Tensor<double, Eigen::aligned_allocator<double> >; A = Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > >; TiledArray::Tensor< <template-parameter-1-1>, <template-parameter-1-2> >::Tensor_ = TiledArray::Tensor<TiledArray::Tensor<double, Eigen::aligned_allocator<double> >, Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > > >]::<lambda(TiledArray::Tensor<TiledArray::Tensor<double, Eigen::aligned_allocator<double> >, Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > > >::numeric_type)>&; TR = TiledArray::Tensor<double, Eigen::aligned_allocator<double> >; Ts = {TiledArray::Tensor<double, Eigen::aligned_allocator<double> >}; typename std::enable_if<(TiledArray::detail::is_tensor<T2, Ts ...>::value && TiledArray::detail::is_contiguous_tensor<T2, Ts ...>::value)>::type* <anonymous> = 0]: Assertion `!empty(result, tensors...)' failedSo I don't know whether to call this an issue with representation (namely how to specify the initializer list for this scenario), an issue with the SparseArray ctor (should it have actually screened out the tile so it's implicitly stored), or an issue with operations like subtraction which don't know what to do with the empty inner tensor.
Not sure how to describe this/where to attribute blame. Also, this isn't a pressing issue as I think it's unlikely to arise outside of unit testing.
This issue concerns how ToTs are represented when an entire independent index is screened out. This is probably easiest to explain with an example.
Consider the following two ToTs as printed by TA. The first ToT:
0: [ [0,0], [1,2] ) { [0,0]:[ [0], [2] ) { 1 2 } [0,1]:[ [0], [2] ) { 7 8 } } 2: [ [1,0], [2,2] ) { [1,0]:[ [0], [2] ) { 0 2 } [1,1]:[ [0], [2] ) { 6 8 } } 7: [ [3,2], [4,3] ) { [3,2]:[ [0], [2] ) { 12 13 } }This tensor is made with make_array and a lambda. As you can see when TA hits the screened out independent indices (1, 3,4,5, and 6) it implicitly stores the tiles associated with those indices. Now consider another tensor:
0: [ [0,0], [1,2] ) { [0,0]:[ [0], [2] ) { 1 2 } [0,1]:[ [0], [2] ) { 7 8 } } 1: [ [0,2], [1,3] ) { [0,2]:[ [], [] ) { } } 2: [ [1,0], [2,2] ) { [1,0]:[ [0], [2] ) { 0 2 } [1,1]:[ [0], [2] ) { 6 8 } } 3: [ [1,2], [2,3] ) { [1,2]:[ [], [] ) { } } 4: [ [2,0], [3,2] ) { [2,0]:[ [], [] ) { } [2,1]:[ [], [] ) { } } 5: [ [2,2], [3,3] ) { [2,2]:[ [], [] ) { } } 6: [ [3,0], [4,2] ) { [3,0]:[ [], [] ) { } [3,1]:[ [], [] ) { } } 7: [ [3,2], [4,3] ) { [3,2]:[ [0], [2] ) { 12 13 } }This tensor is created with an initializer list like:
Note you have to put the zero place-holder inner tensors there as all elements of the outer tensors need to be initialized. Also note you can't do something like: inner_tile zero{TA::Range{1}, {0}} because that's a different shape (you're now saying the independent index maps to a single dependent index, whose value is zero as opposed to mapping to nothing). The intent is for these tensors to be the same, but attempting to subtract them gives an error:
TiledArray/tensor/kernels.h:422: void TiledArray::detail::tensor_init(Op&&, TR&, const Ts& ...) [with Op = TiledArray::Tensor< <template-parameter-1-1>, <template-parameter-1-2> >::neg() const [with T = TiledArray::Tensor<double, Eigen::aligned_allocator<double> >; A = Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > >; TiledArray::Tensor< <template-parameter-1-1>, <template-parameter-1-2> >::Tensor_ = TiledArray::Tensor<TiledArray::Tensor<double, Eigen::aligned_allocator<double> >, Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > > >]::<lambda(TiledArray::Tensor<TiledArray::Tensor<double, Eigen::aligned_allocator<double> >, Eigen::aligned_allocator<TiledArray::Tensor<double, Eigen::aligned_allocator<double> > > >::numeric_type)>&; TR = TiledArray::Tensor<double, Eigen::aligned_allocator<double> >; Ts = {TiledArray::Tensor<double, Eigen::aligned_allocator<double> >}; typename std::enable_if<(TiledArray::detail::is_tensor<T2, Ts ...>::value && TiledArray::detail::is_contiguous_tensor<T2, Ts ...>::value)>::type* <anonymous> = 0]: Assertion `!empty(result, tensors...)' failedSo I don't know whether to call this an issue with representation (namely how to specify the initializer list for this scenario), an issue with the SparseArray ctor (should it have actually screened out the tile so it's implicitly stored), or an issue with operations like subtraction which don't know what to do with the empty inner tensor.