Change:
'symmetric_pd', NO_ARGS, [skipIfNoLapack]), ('logdet', lambda: make_nonzero_det(random_fullrank_matrix_distinct_singular_value(S), 1, 0), NO_ARGS, 'distinct_singular_values', NO_ARGS, [skipIfNoLapack]), <del> ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1_pos_det', NO_ARGS, [skipIfNoLapack], [1]), </del> <ins> ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1_pos_det', NO_ARGS, [skipIfNoLapack], itemgetter(1)), </ins> ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), -1), NO_ARGS, <del> '1x1_neg_det', NO_ARGS, [skipIfNoLapack], [1]), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, 'pos_det', NO_ARGS, [skipIfNoLapack], [1]), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), -1), NO_ARGS, 'neg_det', NO_ARGS, [skipIfNoLapack], [1]), </del> <ins> '1x1_neg_det', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, 'pos_det', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), -1), NO_ARGS, 'neg_det', NO_ARGS, [skipIfNoLapack], itemgetter(1)), </ins> ('slogdet', lambda: make_nonzero_det(random_symmetric_matrix(S)), NO_ARGS, <del> 'symmetric', NO_ARGS, [skipIfNoLapack], [1]), ('slogdet', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', NO_ARGS, [skipIfNoLapack], [1]), </del> <ins> 'symmetric', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', NO_ARGS, [skipIfNoLapack], itemgetter(1)), </ins> ('slogdet', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, <del> 'distinct_singular_values', NO_ARGS, [skipIfNoLapack], [1]), </del> <ins> 'distinct_singular_values', NO_ARGS, [skipIfNoLapack], itemgetter(1)), </ins> ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', NO_ARGS, [skipIfNoLapack]), <del> ('svd', lambda: random_fullrank_matrix_distinct_singular_value(M), NO_ARGS, 'large', NO_ARGS, [skipIfNoLapack]), </del> <ins> ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], NO_ARGS, 'wide', NO_ARGS, [skipIfNoLapack]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], NO_ARGS, 'tall', NO_ARGS, [skipIfNoLapack]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], (False,), 'wide_all', NO_ARGS, [skipIfNoLapack], lambda usv: (usv[0], usv[1], usv[2][:, :(S - 2)])), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], (False,), 'tall_all', NO_ARGS, [skipIfNoLapack], lambda usv: (usv[0][:, :(S - 2)], usv[1], usv[2])), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(M), NO_ARGS, 'large', NO_ARGS, [skipIfNoLapack]), </ins> ('gesv', (S, S), ((S, S),), '', NO_ARGS, [skipIfNoLapack]), ('fill_', (S, S, S), (1,), 'number'), ('fill_', (), (1,), 'number_scalar'),

Discussion:
I am having problem in running the backward propagation with svd. In particular, it seems working when the first dimension of the matrix x (below) is greater than the second. But I get the following error if the first is smaller than the second. PyTorch version: 2018.04.20 Is debug build: No CUDA used to build PyTorch: 8.0.61 OS: Ubuntu 16.04.4 LTS GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.9) 5.4.0 CMake version: Could not collect Python version: 3.5 Is CUDA available: No CUDA runtime version: No CUDA GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA Versions of relevant libraries: [pip3] numpy (1.13.3) [pip3] torch (2018.4.20) [pip3] torchvision (0.2.0) [conda] pytorch 0.3.1 py35cuda8.0.61cudnn7.0.52 pytorch [conda] pytorch-nightly 2018.04.20 py35cuda8.0.61cudnn7.1.21 pytorch [conda] torchvision 0.2.0 py35heaa392f_1 pytorch
Repro'ed on 0.4 branch. works btw.
What does "Repro'ed" means? Should I install from that branch? I need to compute the kernel of a matrix, therefore I need the last columns of the matrix V (which is why I have to pass ).
It's a shorthand for "reproduced", which means that the bug still hasn't been fixed. Sorry, we'll try to get to it soon.