import torch from torch._six import inf, istuple from functools import reduce from operator import mul, itemgetter import collections from torch.autograd import Variable from torch.testing import make_non_contiguous from common_utils import (skipIfNoLapack, prod_single_zero, random_square_matrix_of_rank, random_symmetric_matrix, random_symmetric_psd_matrix, random_symmetric_pd_matrix, make_nonzero_det, random_fullrank_matrix_distinct_singular_value, set_rng_seed) def index_variable(shape, max_indices): if not isinstance(shape, tuple): shape = (shape,) index = torch.rand(*shape).mul_(max_indices).floor_().long() return index def index_perm_variable(shape, max_indices): if not isinstance(shape, tuple): shape = (shape,) index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape) return index def gather_variable(shape, index_dim, max_indices, duplicate=False): assert len(shape) == 2 assert index_dim < 2 batch_dim = 1 - index_dim index = torch.LongTensor(*shape) for i in range(shape[index_dim]): index.select(index_dim, i).copy_( torch.randperm(max_indices)[:shape[batch_dim]]) if duplicate: index.select(batch_dim, 0).copy_(index.select(batch_dim, 1)) return index def bernoulli_scalar(): return torch.tensor(0, dtype=torch.uint8).bernoulli_() def mask_not_all_zeros(shape): assert len(shape) > 0 while True: result = torch.randn(shape).gt(0) if result.sum() > 0: return result def uniform_scalar(offset=0, requires_grad=False): v = torch.rand(()) + offset v.requires_grad = requires_grad return v def normal_scalar_clamp(amin, amax, requires_grad=False): v = torch.randn(()).clamp(amin, amax) v.requires_grad = requires_grad return v def prod_zeros(dim_size, dim_select): assert len(dim_select) == 2 result = torch.randn(dim_size, dim_size, dim_size) result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_() result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_() result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_() return result non_differentiable = collections.namedtuple('non_differentiable', ['tensor']) class dont_convert(tuple): pass class NoArgsClass(object): def __iter__(self): return self def __next__(self): raise StopIteration() next = __next__ # Python 2 compatibility def __len__(self): return 0 NO_ARGS = NoArgsClass() L = 20 M = 10 S = 5 # ( # method name, # input size/constructing fn, # args (tuple represents shape of a tensor arg), # test variant name (will be used at test name suffix), // optional # indices for possible dim arg, // optional # fn mapping output to part that should be gradcheck'ed, // optional # ) def method_tests(): set_rng_seed(0) return [ ('add', (S, S, S), ((S, S, S),)), ('add', (S, S, S), ((S, S),), 'broadcast_rhs'), ('add', (S, S), ((S, S, S),), 'broadcast_lhs'), ('add', (S, 1, S), ((M, S),), 'broadcast_all'), ('add', (), ((),), 'scalar'), ('add', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('add', (), ((S, S, S),), 'scalar_broadcast_lhs'), ('add', (S, S, S), (3.14,), 'constant'), ('add', (), (3.14,), 'scalar_constant'), ('__radd__', (S, S, S), (3.14,), 'constant'), ('__radd__', (), (3.14,), 'scalar_constant'), ('sub', (S, S, S), ((S, S, S),)), ('sub', (S, S, S), ((S, S),), 'broadcast_rhs'), ('sub', (S, S), ((S, S, S),), 'broadcast_lhs'), ('sub', (S, 1, S), ((M, S),), 'broadcast_all'), ('sub', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('sub', (), ((S, S, S),), 'scalar_broadcast_lhs'), ('sub', (S, S, S), (3.14,), 'constant'), ('sub', (), (3.14,), 'scalar_constant'), ('__rsub__', (S, S, S), (3.14,), 'constant'), ('__rsub__', (), (3.14,), 'scalar_constant'), ('mul', (S, S, S), ((S, S, S),)), ('mul', (), ((),), 'scalar'), ('mul', (S, S, S), ((S, S),), 'broadcast_rhs'), ('mul', (S, S), ((S, S, S),), 'broadcast_lhs'), ('mul', (S, 1, S), ((M, S),), 'broadcast_all'), ('mul', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('mul', (), ((S, S, S),), 'scalar_broadcast_lhs'), ('mul', (S, S, S), (3.14,), 'constant'), ('mul', (), (3.14,), 'scalar_constant'), ('__rmul__', (S, S, S), (3.14,), 'constant'), ('__rmul__', (), (3.14,), 'scalar_constant'), ('div', (S, S, S), (torch.rand(S, S, S) + 0.1,)), ('div', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs'), ('div', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs'), ('div', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all'), ('div', (), (uniform_scalar(0.1),), 'scalar'), ('div', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs'), ('div', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs'), ('div', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant'), ('__rdiv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant'), ('div', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant'), ('__rdiv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant'), ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(S, S, S) + 0.1,)), ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(1,) + 0.1,), 'broadcast_rhs'), ('pow', torch.rand(1,) + 1e-3, (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs'), ('pow', torch.rand(S, 1, S) + 1e-3, (torch.rand(1, S, 1) + 0.1,), 'broadcast_all'), ('pow', uniform_scalar(1e-3, requires_grad=True), (uniform_scalar(0.1),), 'scalar'), ('pow', torch.rand(S, S, S) + 1e-3, (uniform_scalar(0.1),), 'scalar_broadcast_rhs'), ('pow', uniform_scalar(1e-3, requires_grad=True), (torch.rand(S, S, S) + 0.1,), 'scalar_broadcast_lhs'), ('pow', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant'), ('__rpow__', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant'), ('pow', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant'), ('__rpow__', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant'), ('transpose', (1, 2, 3), (1, 2), 'dim', [0, 1]), ('transpose', (), (0, 0), 'scalar'), ('transpose', (1,), (0, 0), '1d'), ('transpose', torch.rand(L, L), (0, 1), '2d'), ('transpose', torch.rand(S, S, S), (2, 0), '3d'), ('t', (1, 2), NO_ARGS), ('view', (S, S, S), (S * S, S),), ('view', (S, S, S), (torch.Size([S * S, S]),), 'size'), ('view', (S,), (S,), '1d'), ('view', (), (dont_convert(()),), 'scalar_to_scalar'), ('view', (), (1,), 'scalar_to_1d'), ('reshape', (S, S, S), (S * S, S),), ('reshape', (S, S, S), (torch.Size([S * S, S]),), 'size'), ('reshape', (S,), (S,), '1d'), ('reshape', (), (dont_convert(()),), 'scalar_to_scalar'), ('reshape', (), (1,), 'scalar_to_1d'), ('reshape_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)), ('reshape_as', (), (non_differentiable(torch.tensor(42.)),), 'scalar'), ('reshape_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'), ('flip', (S, S, S), ([0],), 'd0'), ('flip', (S, S, S), ([0, 1, 2],), 'd012'), ('flip', (S, S, S), ([0, 2],), 'd02'), ('flip', (S, S, S), ([2, 0],), 'd20'), ('flip', (S, S, S), ([-1],), 'neg_d'), ('roll', (S, S, S), (0, 0), 'd0'), ('roll', (S, S, S), (1, 2), 'd12'), ('roll', (S, S, S), (0, 2,), 'd02'), ('roll', (S, S, S), (2, 0,), 'd20'), ('roll', (S, S, S), (-1, 0), 'neg_shift'), ('roll', (S, S, S), (10000, 1), 'loop_shift'), ('roll', (S, S, S), (2,), 'flattened'), ('roll', (S, S, S), ([1, 2, -1], [0, 1, 2]), 'three_dims'), ('rot90', (S, S, S), (1, [0, 1],), 'k1_d01'), ('rot90', (S, S, S), (1, [1, 2],), 'k1_d12'), ('rot90', (S, S, S), (1, [1, -1],), 'k1_neg_d'), ('rot90', (S, S, S), (), 'default'), ('view_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)), ('view_as', (), (non_differentiable(torch.tensor(5.5)),), 'scalar'), ('view_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'), ('expand', (S, 1, 1), (S, S, S)), ('expand', (torch.Size([S, 1, S]),), (S, S, S), 'size'), ('expand', (S, 1), (S, S, S), 'new_dim'), ('expand', (1,), (S, S, S), '1_element'), ('expand', (1, S), (1, 1, S), 'new_dim_front_old_front_1'), ('expand', (), (dont_convert(()),), 'scalar_to_scalar'), ('expand', (), (1, 3, 2), 'scalar_to_dims'), ('expand_as', (S, 1, 1), (torch.rand(S, S, S),)), ('exp', (S, S, S), NO_ARGS), ('exp', (), NO_ARGS, 'scalar'), ('expm1', (S, S, S), NO_ARGS), ('expm1', (), NO_ARGS, 'scalar'), ('erf', torch.rand(S, S, S), NO_ARGS), ('erf', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar'), ('erfc', torch.rand(S, S, S), NO_ARGS), ('erfc', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar'), ('erfinv', torch.rand(S, S, S).clamp(-0.9, 0.9), NO_ARGS), ('erfinv', normal_scalar_clamp(-0.9, 0.9, requires_grad=True), NO_ARGS, 'scalar'), ('log', torch.rand(S, S, S) + 1e-2, NO_ARGS), ('log', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar'), ('log10', torch.rand(S, S, S) + 1e-2, NO_ARGS), ('log10', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar'), ('log1p', torch.rand(S, S, S), NO_ARGS), ('log1p', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar'), ('log2', torch.rand(S, S, S) + 1e-2, NO_ARGS), ('log2', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar'), ('tanh', (S, S, S), NO_ARGS), ('tanh', (), NO_ARGS, 'scalar'), ('sigmoid', (S, S, S), NO_ARGS), ('sigmoid', (), NO_ARGS, 'scalar'), ('sinh', (S, S, S), NO_ARGS), ('sinh', (), NO_ARGS, 'scalar'), ('cosh', (S, S, S), NO_ARGS), ('cosh', (), NO_ARGS, 'scalar'), ('abs', (S, S, S), NO_ARGS), ('abs', (), NO_ARGS, 'scalar'), ('clamp', (S, S, S), (0, 1)), ('clamp', (S, S, S), (None, 0.5), 'min'), ('clamp', (S, S, S), (0.5, None), 'max'), ('clamp', (), (0, 1), 'scalar'), ('clamp', (), (None, 0.5), 'min_scalar'), ('clamp', (), (0.5, None), 'max_scalar'), ('sqrt', torch.rand(S, S, S) + 5e-4, NO_ARGS), ('sqrt', uniform_scalar(5e-4, requires_grad=True), NO_ARGS, 'scalar'), ('sin', (S, S, S), NO_ARGS), ('sin', (), NO_ARGS, 'scalar'), ('cos', (S, S, S), NO_ARGS), ('cos', (), NO_ARGS, 'scalar'), ('tan', torch.randn(S, S, S).clamp(-1, 1), NO_ARGS), ('asin', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS), ('acos', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS), ('atan', (S, S, S), NO_ARGS), ('atan', (), NO_ARGS, 'scalar'), ('atan2', (S, S, S), ((S, S, S),)), ('atan2', (), ((),), 'scalar'), ('atan2', (S, S, S), ((S,),), 'broadcast_rhs'), ('atan2', (S,), ((S, S, S),), 'broadcast_lhs'), ('atan2', (S, 1, S), ((S, S),), 'broadcast_all'), ('reciprocal', torch.rand(S, S, S) + 0.1, NO_ARGS), ('reciprocal', uniform_scalar(0.1, requires_grad=True), NO_ARGS, 'scalar'), ('round', (S, S, S), NO_ARGS), ('round', (), NO_ARGS, 'scalar'), ('sign', (S, S, S), NO_ARGS), ('sign', (), NO_ARGS, 'scalar'), ('trunc', (S, S, S), NO_ARGS), ('trunc', (), NO_ARGS, 'scalar'), ('floor', (S, S, S), NO_ARGS), ('floor', (), NO_ARGS, 'scalar'), ('ceil', (S, S, S), NO_ARGS), ('ceil', (), NO_ARGS, 'scalar'), ('rsqrt', torch.rand(S, S, S) + 1e-2, NO_ARGS), ('rsqrt', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar'), ('frac', (S, S, S), NO_ARGS), ('frac', (), NO_ARGS, 'scalar'), ('fmod', (S, S, S), (1.5,)), ('fmod', (), (1.5,), 'scalar'), ('fmod', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'), ('fmod', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'), ('fmod', (S, S, S), (non_differentiable(torch.rand(S) + 1.5),), 'tensor_broadcast_rhs'), ('fmod', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'), ('fmod', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'), ('fmod', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'), ('fmod', (S, S, S), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor_broadcast_rhs'), ('remainder', (S, S, S), (1.5,)), ('remainder', (), (1.5,), 'scalar'), ('remainder', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'), ('remainder', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'), ('remainder', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'), ('remainder', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'), ('remainder', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'), ('lerp', (S, S, S), ((S, S, S), 0.4), 'scalar_no_broadcast'), ('lerp', (S, S, S), ((S,), 0.4), 'broadcast_rhs'), ('lerp', (S,), ((S, S, S), 0.4), 'broadcast_lhs'), ('lerp', (S, 1, S), ((S, S), 0.4), 'broadcast_all'), ('lerp', (), ((), 0.4), 'scalar'), ('lerp', (S, S, S), ((), 0.4), 'scalar_broadcast_rhs'), ('lerp', (), ((S, S, S), 0.4), 'scalar_broadcast_lhs'), ('max', (S, S, S), NO_ARGS), ('max', (S, S, S), (1,), 'dim', [0]), ('max', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('max', (), NO_ARGS, 'scalar'), ('max', (), (0,), 'scalar_dim', [0]), ('max', (), (0, True,), 'scalar_keepdim_dim', [0]), ('max', (S, S, S), ((S, S, S),), 'elementwise'), ('max', (S, S, S), ((S,),), 'elementwise_broadcast_rhs'), ('max', (S,), ((S, S, S),), 'elementwise_broadcast_lhs'), ('max', (S, 1, S), ((S, S),), 'elementwise_broadcast_all'), ('max', (), ((),), 'scalar_elementwise'), ('max', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs'), ('max', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs'), ('min', (S, S, S), NO_ARGS), ('min', (S, S, S), (1,), 'dim', [0]), ('min', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('min', (), NO_ARGS, 'scalar'), ('min', (), (0,), 'scalar_dim', [0]), ('min', (), (0, True,), 'scalar_keepdim_dim', [0]), ('min', (S, S, S), ((S, S, S),), 'elementwise'), ('min', (S, S, S), ((S,),), 'elementwise_broadcast_rhs'), ('min', (S,), ((S, S, S),), 'elementwise_broadcast_lhs'), ('min', (S, 1, S), ((S, S),), 'elementwise_broadcast_all'), ('min', (), ((),), 'scalar_elementwise'), ('min', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs'), ('min', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs'), ('mean', (S, S, S), NO_ARGS), ('mean', (S, S, S), (1,), 'dim', [0]), ('mean', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('mean', (), NO_ARGS, 'scalar'), ('mean', (), (0,), 'scalar_dim', [0]), ('mean', (), (0, True,), 'scalar_keepdim_dim', [0]), ('kthvalue', (S, S, S), (2,)), ('kthvalue', (), (1,), 'scalar'), ('kthvalue', (S, S, S), (2, 1,), 'dim', [1]), ('kthvalue', (), (1, 0,), 'scalar_dim', [1]), ('kthvalue', (S, S, S), (2, 1, True,), 'keepdim_dim', [1]), ('kthvalue', (), (1, 0, True), 'scalar_keepdim_dim', [1]), ('kthvalue', (S,), (2, 0,), 'dim_1d', [1]), ('kthvalue', (S,), (2, 0, True,), 'keepdim_dim_1d', [1]), ('median', (S, S, S), NO_ARGS), ('median', (S, S, S), (1,), 'dim', [0]), ('median', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('median', (), NO_ARGS, 'scalar'), ('median', (), (0,), 'scalar_dim', [0]), ('median', (), (0, True,), 'scalar_keepdim_dim', [0]), ('mode', (S, S, S), NO_ARGS), ('mode', (S, S, S), (1,), 'dim', [0]), ('mode', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('mode', (), NO_ARGS, 'scalar'), ('mode', (), (0,), 'scalar_dim', [0]), ('mode', (), (0, True,), 'scalar_keepdim_dim', [0]), ('sum', (S, S, S), NO_ARGS), ('sum', (S, S, S), (1,), 'dim', [0]), ('sum', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('sum', (), NO_ARGS, 'scalar'), ('sum', (), (0,), 'scalar_dim', [0]), ('sum', (), (0, True,), 'scalar_keepdim_dim', [0]), ('sum', (S, S, S), ([1, 2],), 'multi_dim'), ('sum', (S, S, S), ([1, 2], True,), 'multi_dim_keepdim'), ('prod', (S, S, S), NO_ARGS), ('prod', (S, S, S), (1,), 'dim', [0]), ('prod', (S, S, S), (1, True,), 'keepdim_dim', [0]), ('prod', (), NO_ARGS, 'scalar'), ('prod', (), (0,), 'scalar_dim', [0]), ('prod', (), (0, True,), 'scalar_keepdim_dim', [0]), ('prod', prod_zeros(S, [0, 1]), NO_ARGS, 'zerodims2'), ('prod', prod_zeros(S, [0, 2]), NO_ARGS, 'zerodims1'), ('prod', prod_zeros(S, [1, 2]), NO_ARGS, 'zerodims0'), ('prod', prod_zeros(S, [0, 1]), (1,), 'zeros_dims2', [0]), ('prod', prod_zeros(S, [0, 2]), (1,), 'zeros_dims1', [0]), ('prod', prod_zeros(S, [1, 2]), (1,), 'zeros_dims0', [0]), ('prod', prod_zeros(S, [0, 1]), (1, True), 'keepdim_zeros_dims2', [0]), ('prod', prod_zeros(S, [0, 2]), (1, True), 'keepdim_zeros_dims1', [0]), ('prod', prod_zeros(S, [1, 2]), (1, True), 'keepdim_zeros_dims0', [0]), ('prod', prod_single_zero(S), NO_ARGS, 'single_zero'), ('prod', (torch.tensor(0., requires_grad=True)), NO_ARGS, 'scalar_zero'), ('prod', (torch.tensor(0., requires_grad=True)), (0,), 'scalar_dim_zero', [0]), ('prod', (torch.tensor(0., requires_grad=True)), (0, True,), 'scalar_keepdim_dim_zero', [0]), ('var', (S, S, S), NO_ARGS), ('var', (S, S, S), (1,), 'dim', [0]), ('var', (S, S, S), (1, True, True), 'keepdim_dim', [0]), ('var', (S,), (0,), 'dim_1d', [0]), ('var', (S,), (0, True, True), 'keepdim_dim_1d', [0]), ('std', (S, S, S), NO_ARGS), ('std', (S, S, S), (1,), 'dim', [0]), ('std', (S, S, S), (1, True, True), 'keepdim_dim', [0]), ('std', (S,), (0,), 'dim_1d', [0]), ('std', (S,), (0, True, True), 'keepdim_dim_1d', [0]), ('renorm', (S, S, S), (2, 1, 0.5), 'dim', [1]), ('renorm', (S, S, S), (1, 2, 3), 'norm_1'), ('renorm', (S, S, S), (inf, 2, 0.5), 'norm_inf'), ('repeat', (S,), (2,), 'single_number'), ('repeat', (), (2, 3), 'scalar'), ('repeat', (2, 2), (3, 2)), ('repeat', (2, 2), (1, 3, 1, 2), 'unsqueeze'), ('cumsum', (S, S, S), (0,), 'dim0', [0]), ('cumsum', (S, S, S), (1,), 'dim1', [0]), ('cumsum', (S, S, S), (1,), 'dim1_cast', [0], (), lambda x: x, {'dtype': torch.float64}), ('cumsum', (), (0,), 'dim0_scalar', [0]), ('cumprod', (S, S, S), (0,)), ('cumprod', (S, S, S), (1,), 'dim1', [0]), ('cumprod', (), (0,), 'scalar'), ('cumprod', (torch.tensor(0., requires_grad=True)), (0,), 'scalar_zeros'), ('cumprod', prod_zeros(S, [0, 1]), (1,), 'zeros_dim2', [0]), ('cumprod', prod_zeros(S, [0, 2]), (1,), 'zeros_dim1', [0]), ('cumprod', prod_zeros(S, [1, 2]), (1,), 'zeros_dim0', [0]), ('cumprod', prod_zeros(S, [1, 2]), (1,), 'zeros_dim0_cast', [0], (), lambda x: x, {'dtype': torch.float64}), ('unfold', (), (0, 1, 1), 'scalar', [0]), ('unfold', (S, S, S, S), (1, 3, 1), '', [0]), ('unfold', (S, S, S), (2, 3, 2), 'lastdim', [0]), ('addmm', (S, M), ((S, S), (S, M)),), ('addmm', (1,), ((S, S), (S, M)), 'broadcast_lhs'), ('addmm', (S, M), ((S, S), (S, M)), 'coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addmm', (1,), ((S, S), (S, M)), 'broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addmm', (), ((S, S), (S, M)), 'scalar_broadcast_lhs'), ('addmm', (), ((S, S), (S, M)), 'scalar_broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addbmm', (S, M), ((S, S, S), (S, S, M)),), ('addbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs'), ('addbmm', (S, M), ((S, S, S), (S, S, M)), 'coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addbmm', (), ((S, S, S), (S, S, M)), 'scalar_broadcast_lhs'), ('addbmm', (), ((S, S, S), (S, S, M)), 'scalar_broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('baddbmm', (S, S, M), ((S, S, S), (S, S, M)),), ('baddbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs'), ('baddbmm', (S, S, M), ((S, S, S), (S, S, M)), 'coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('baddbmm', (1,), ((S, S, S), (S, S, M)), 'broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('baddbmm', (), ((S, S, S), (S, S, M)), 'scalar_broadcast_lhs'), ('baddbmm', (), ((S, S, S), (S, S, M)), 'scalar_broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addmv', (S,), ((S, M), (M,)),), ('addmv', (1,), ((S, M), (M,)), 'broadcast_lhs'), ('addmv', (S,), ((S, M), (M,)), 'coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addmv', (1,), ((S, M), (M,)), 'broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addmv', (), ((S, M), (M,)), 'scalar_broadcast_lhs'), ('addmv', (), ((S, M), (M,)), 'scalar_broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addr', (S, M), ((S,), (M,)),), ('addr', (), ((S,), (M,)), 'broadcast_lhs'), ('addr', (S, M), ((S,), (M,)), 'coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('addr', (), ((S,), (M,)), 'broadcast_lhs_coef', (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}), ('dot', (L,), ((L,),),), ('mm', (S, M), ((M, S),)), ('bmm', (M, S, M), ((M, M, S),)), ('mv', (S, M), ((M,),)), ('ger', (S,), ((M,),)), ('matmul', (L,), ((L,),),), ('matmul', (S, M), ((M,),), "2d_1d"), ('matmul', (M, ), ((M, S),), "1d_2d"), ('matmul', (S, M), ((M, S),), "2d_2d"), ('matmul', (S, S, M, M), ((S, S, M, S),), "4d_4d"), ('matmul', (S, S, M, M), ((M,),), "4d_1d"), ('matmul', (M,), ((S, S, M, S),), "1d_4d"), ('matrix_power', (S, S), [2], "n=2"), ('matrix_power', (S, S, S), [3], "n=3"), ('matrix_power', (S, S, S), [1], "n=1"), ('matrix_power', (S, S, S), [0], "n=0"), ('matrix_power', lambda: random_fullrank_matrix_distinct_singular_value(S), [-1], "n=-1", NO_ARGS, [skipIfNoLapack]), ('matrix_power', lambda: random_fullrank_matrix_distinct_singular_value(S), [-3], "n=-3", NO_ARGS, [skipIfNoLapack]), ('matrix_power', lambda: random_fullrank_matrix_distinct_singular_value(S, S), [-2], "n=-2", NO_ARGS, [skipIfNoLapack]), ('mvlgamma', torch.empty(S,).uniform_(0.5, 1), [1], "p=1"), ('mvlgamma', torch.empty(S,).uniform_(1, 2), [2], "p=2"), ('mvlgamma', torch.empty(S, S).uniform_(1.5, 3), [3], "p=3"), ('mvlgamma', torch.empty(S, S).uniform_(2.5, 5), [5], "p=5"), ('addcmul', (S, S), ((S, S), (S, S))), ('addcmul', (S, S), ((S, 1), (1, S)), 'broadcast_rhs'), ('addcmul', (1,), ((S, S, 1), (1, S)), 'broadcast_all'), ('addcmul', (S, S), ((S, S), (S, S)), 'scale', (), (), lambda x: x, {'value': 0.5}), ('addcmul', (S, S), ((S, 1), (1, S)), 'scale_broadcast_rhs', (), (), lambda x: x, {'value': 0.5}), ('addcmul', (1,), ((S, S, 1), (1, S)), 'scale_broadcast_all', (), (), lambda x: x, {'value': 0.5}), ('addcmul', (), ((), ()), 'scalar'), ('addcmul', (S, S), ((), ()), 'scalar_broadcast_rhs'), ('addcmul', (), ((S, S, 1), (1, S)), 'scalar_broadcast_lhs'), ('addcmul', (), ((), ()), 'scalar_scale', (), (), lambda x: x, {'value': 0.5}), ('addcmul', (S, S), ((), ()), 'scalar_scale_broadcast_rhs', (), (), lambda x: x, {'value': 0.5}), ('addcmul', (), ((S, S, 1), (1, S)), 'scalar_scale_broadcast_lhs', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (S, S), ((S, S), (S, S))), ('addcdiv', (S, S), ((S, 1), (1, S)), 'broadcast_rhs'), ('addcdiv', (1,), ((S, S, 1), (1, S)), 'broadcast_all'), ('addcdiv', (S, S), ((S, S), (S, S)), 'scale', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (S, S), ((S, 1), (1, S)), 'scale_broadcast_rhs', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (1,), ((S, S, 1), (1, S)), 'scale_broadcast_all', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (), ((), ()), 'scalar'), ('addcdiv', (S, S), ((), ()), 'scalar_broadcast_rhs'), ('addcdiv', (), ((S, S, 1), (1, S)), 'scalar_broadcast_lhs'), ('addcdiv', (), ((), ()), 'scalar_scale', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (S, S), ((), ()), 'scalar_scale_broadcast_rhs', (), (), lambda x: x, {'value': 0.5}), ('addcdiv', (), ((S, S, 1), (1, S)), 'scalar_scale_broadcast_lhs', (), (), lambda x: x, {'value': 0.5}), ('zero_', (S, S, S), NO_ARGS), ('zero_', (), NO_ARGS, 'scalar'), ('logsumexp', (S, S), (1,)), ('logsumexp', (), (0,), 'scalar'), ('norm', (S, S), (), 'default'), ('norm', (S, S), (2,), '2'), ('norm', (S, S), (0,), '0'), ('norm', (S, S), (0.5,), '0_5'), ('norm', (S, S), (1,), '1'), ('norm', (S, S), (3,), '3'), ('norm', (S, S), (inf,), 'inf'), ('norm', (S, S), (-inf,), '-inf'), ('norm', (S, S), ('fro',), 'fro_default'), ('norm', (S, S), ('fro', [0, 1],), 'fro'), ('norm', (S, S), ('nuc',), 'nuc', NO_ARGS, [skipIfNoLapack]), ('norm', (S, S), (-1,), 'neg_1'), ('norm', (S, S), (-2,), 'neg_2'), ('norm', (S, S), (-0.5,), 'neg_0_5'), ('norm', (S, S), (-1.5,), 'neg_1_5'), ('norm', (S, S), (-2, 1,), 'neg_2_2_dim', [1]), ('norm', (S, S), (-1, 1,), 'neg_1_2_dim', [1]), ('norm', (S, S), (0, 1,), '0_2_dim', [1]), ('norm', (S, S), (1, 1,), '1_2_dim', [1]), ('norm', (S, S), (2, 1,), '2_2_dim', [1]), ('norm', (S, S), (3, 1,), '3_2_dim', [1]), ('norm', (S, S), (inf, 1,), 'inf_2_dim'), ('norm', torch.rand(S, S, S) + 5e-2, (1.5,), '1_5_default'), ('norm', (S, S, S), (2, 1), '2_dim', [1]), ('norm', (S, S, S), (3, 1), '3_dim', [1]), ('norm', torch.rand(S, S, S) + 5e-2, (1.5, 1), '1_5_dim', [1]), ('norm', (S, S, S), (2, 1, True), 'keepdim_2_dim', [1]), ('norm', (S, S, S), (3, 1, True), 'keepdim_3_dim', [1]), ('norm', torch.rand(S, S, S) + 5e-2, (1.5, 1, True), 'keepdim_1_5_dim', [1]), ('norm', (), (2, 0), '2_dim_scalar', [1]), ('norm', (), (3, 0), '3_dim_scalar', [1]), ('norm', (), (2, 0, True), 'keepdim_2_dim_scalar', [1]), ('norm', (), (3, 0, True), 'keepdim_3_dim_scalar', [1]), ('clone', (S, M, S), NO_ARGS), ('clone', (), NO_ARGS, 'scalar'), ('dist', (S, S, S), ((S, S, S),)), ('dist', (S, S, S), ((S,),), 'broadcast_rhs'), ('dist', (S,), ((S, S, S),), 'broadcast_lhs'), ('dist', (S, 1, S), ((S, S),), 'broadcast_all'), ('dist', (), ((),), 'scalar'), ('dist', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('dist', (), ((S, S, S),), 'scalar_broadcast_lhs'), ('dist', (S, S, S), ((S, S, S), 4), '4'), ('dist', (S, S, S), ((S,), 4), '4_broadcast_rhs'), ('dist', (S,), ((S, S, S), 4), '4_broadcast_lhs'), ('dist', (S, 1, S), ((S, S), 4), '4_broadcast_all'), ('dist', (), ((), 4), 'scalar_4'), ('dist', (S, S, S), ((), 4), 'scalar_4_broadcast_rhs'), ('dist', (), ((S, S, S), 4), 'scalar_4_broadcast_lhs'), ('diag', (M, M), NO_ARGS, '2d'), ('diag', (3, 5), NO_ARGS, '2d_wide'), ('diag', (3, 5), (2,), '2d_wide_pos'), ('diag', (3, 5), (-2,), '2d_wide_neg'), ('diag', (5, 3), NO_ARGS, '2d_tall'), ('diag', (5, 3), (2,), '2d_tall_pos'), ('diag', (5, 3), (-2,), '2d_tall_neg'), ('diag', (M,), NO_ARGS, '1d'), ('diag', (M, M), (1,), '2d_1'), ('diag', (M, M), (2,), '2d_2'), ('diag_embed', (S, S), NO_ARGS), ('diagonal', (M, M), NO_ARGS, '2d'), ('diagonal', (3, 5), NO_ARGS, '2d_wide'), ('diagonal', (3, 5), (2,), '2d_wide_pos'), ('diagonal', (3, 5), (-2,), '2d_wide_neg'), ('diagonal', (5, 3), NO_ARGS, '2d_tall'), ('diagonal', (5, 3), (2,), '2d_tall_pos'), ('diagonal', (5, 3), (-2,), '2d_tall_neg'), ('diagonal', (M, M), (1,), '2d_1'), ('diagonal', (M, M), (2,), '2d_2'), ('diagonal', (M, M, M), (1, 1, 2), '3d_1'), ('diagonal', (M, M, M), (2, 0, 1), '3d_2'), ('diagonal', (M, M, M), (-2, 0, 1), '3d_3'), ('tril', (M, M), NO_ARGS), ('tril', (M, M), (2,), 'idx'), ('tril', (S, M, M), NO_ARGS, 'batched'), ('tril', (S, M, M), (2,), 'batched_idx'), ('tril', (3, 3, S, S), NO_ARGS, 'more_batched'), ('triu', (M, M), NO_ARGS), ('triu', (M, M), (2,), 'idx'), ('triu', (S, M, M), NO_ARGS, 'batched'), ('triu', (S, M, M), (2,), 'batched_idx'), ('triu', (3, 3, S, S), NO_ARGS, 'more_batched'), ('trace', (M, M), NO_ARGS), ('cross', (S, 3), ((S, 3),)), ('cross', (S, 3, S), ((S, 3, S), 1), 'dim'), ('index_select', (S, S, S), (0, index_variable(2, S)), 'dim', [0]), ('index_select', (), (0, torch.tensor([0], dtype=torch.int64)), 'scalar_mixed_dim', [0]), ('index_select', (), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_dim', [0]), ('index_add', (S, S), (0, index_variable(2, S), (2, S)), 'dim', [0]), ('index_add', (), (0, torch.tensor([0], dtype=torch.int64), (1,)), 'scalar_input_dim', [0]), ('index_add', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim', [0]), ('index_copy', (S, S), (0, index_perm_variable(2, S), (2, S)), 'dim', [0]), ('index_copy', (), (0, torch.tensor([0], dtype=torch.int64), (1,)), 'scalar_input_dim', [0]), ('index_copy', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim', [0]), ('index_fill', (S, S), (0, index_variable(2, S), 2), 'dim', [0]), ('index_fill', (S, S), (0, index_variable(2, S), ()), 'variable_dim', [0]), ('index_fill', (S, S), (0, torch.tensor(0, dtype=torch.int64), 2), 'scalar_index_dim', [0]), ('index_fill', (), (0, torch.tensor([0], dtype=torch.int64), 2), 'scalar_input_dim', [0]), ('index_fill', (), (0, torch.tensor(0, dtype=torch.int64), 2), 'scalar_both_dim', [0]), ('inverse', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', NO_ARGS, [skipIfNoLapack]), ('inverse', lambda: random_fullrank_matrix_distinct_singular_value(S, 2, 3), NO_ARGS, 'batched', NO_ARGS, [skipIfNoLapack]), ('det', (S, S), NO_ARGS, '', NO_ARGS, [skipIfNoLapack]), ('det', (1, 1), NO_ARGS, '1x1', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_symmetric_matrix(S), NO_ARGS, 'symmetric', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_symmetric_psd_matrix(S), NO_ARGS, 'symmetric_psd', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_square_matrix_of_rank(S, S - 2), NO_ARGS, 'dim2_null', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_square_matrix_of_rank(S, 1), NO_ARGS, 'rank1', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_square_matrix_of_rank(S, 2), NO_ARGS, 'rank2', NO_ARGS, [skipIfNoLapack]), ('det', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, 'distinct_singular_values', NO_ARGS, [skipIfNoLapack]), # For `logdet` and `slogdet`, the function at det=0 is not smooth. # We need to exclude tests with det=0 (e.g. dim2_null, rank1, rank2) and use # `make_nonzero_det` to make the random matrices have nonzero det. For # `logdet`, we also set `make_nonzero_det(matrix, sign=1)` to make the # matrix have positive det. ('logdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, '', NO_ARGS, [skipIfNoLapack]), ('logdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1', NO_ARGS, [skipIfNoLapack]), ('logdet', lambda: make_nonzero_det(random_symmetric_matrix(S), 1), NO_ARGS, 'symmetric', NO_ARGS, [skipIfNoLapack]), ('logdet', lambda: make_nonzero_det(random_symmetric_pd_matrix(S), 1), NO_ARGS, '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]), ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1_pos_det', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), -1), NO_ARGS, '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)), ('slogdet', lambda: make_nonzero_det(random_symmetric_matrix(S)), NO_ARGS, 'symmetric', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('slogdet', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, 'distinct_singular_values', NO_ARGS, [skipIfNoLapack], itemgetter(1)), ('symeig', lambda: random_symmetric_matrix(S), (True, False), 'lower', NO_ARGS, [skipIfNoLapack]), ('symeig', lambda: random_symmetric_matrix(S), (True, True), 'upper', NO_ARGS, [skipIfNoLapack]), ('symeig', lambda: random_symmetric_matrix(M), (True, True), 'large', NO_ARGS, [skipIfNoLapack]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', NO_ARGS, [skipIfNoLapack]), ('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]), ('solve', (S, S), (random_fullrank_matrix_distinct_singular_value( S, silent=True),), '', NO_ARGS, [skipIfNoLapack]), ('solve', (S, S, S), (random_fullrank_matrix_distinct_singular_value(S, S, silent=True),), 'batched', NO_ARGS, [skipIfNoLapack]), ('solve', (2, 3, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 3, silent=True),), 'batched_dims', NO_ARGS, [skipIfNoLapack]), ('solve', (2, 2, S, S), (random_fullrank_matrix_distinct_singular_value(S, 1, silent=True),), 'batched_broadcast_A', NO_ARGS, [skipIfNoLapack]), ('solve', (1, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 2, silent=True),), 'batched_broadcast_b', NO_ARGS, [skipIfNoLapack]), ('fill_', (S, S, S), (1,), 'number'), ('fill_', (), (1,), 'number_scalar'), ('fill_', (S, S, S), ((),), 'variable'), ('eq_', (S, S, S), ((S, S, S),)), ('eq_', (S, S, S), ((1,),), 'broadcast_rhs'), ('eq_', (), ((),), 'scalar'), ('eq_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('ne_', (S, S, S), ((S, S, S),)), ('ne_', (S, S, S), ((1,),), 'broadcast_rhs'), ('ne_', (), ((),), 'scalar'), ('ne_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('gt_', (S, S, S), ((S, S, S),)), ('gt_', (S, S, S), ((1,),), 'broadcast_rhs'), ('gt_', (), ((),), 'scalar'), ('gt_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('ge_', (S, S, S), ((S, S, S),)), ('ge_', (S, S, S), ((1,),), 'broadcast_rhs'), ('ge_', (), ((),), 'scalar'), ('ge_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('lt_', (S, S, S), ((S, S, S),)), ('lt_', (S, S, S), ((1,),), 'broadcast_rhs'), ('lt_', (), ((),), 'scalar'), ('lt_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('le_', (S, S, S), ((S, S, S),)), ('le_', (S, S, S), ((1,),), 'broadcast_rhs'), ('le_', (), ((),), 'scalar'), ('le_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('eq_', (S, S, S), (0,), 'pyscalar'), ('ne_', (S, S, S), (0,), 'pyscalar'), ('gt_', (S, S, S), (0,), 'pyscalar'), ('ge_', (S, S, S), (0,), 'pyscalar'), ('le_', (S, S, S), (0,), 'pyscalar'), ('lt_', (), (0,), 'pyscalar'), ('eq_', (), (0,), 'pyscalar_scalar'), ('ne_', (), (0,), 'pyscalar_scalar'), ('gt_', (), (0,), 'pyscalar_scalar'), ('ge_', (), (0,), 'pyscalar_scalar'), ('lt_', (), (0,), 'pyscalar_scalar'), ('le_', (), (0,), 'pyscalar_scalar'), ('permute', (1, 2, 3, 4), (0, 2, 3, 1)), ('permute', (1, 2, 3, 4), (0, -2, -1, 1), 'neg_dim'), ('permute', (), (dont_convert(()),), 'scalar'), ('select', (S, S, S), (1, 2), 'dim', [0]), ('select', (S, S, S), (1, -1), 'wrap_dim', [0]), ('select', (S,), (0, 2), '1d'), ('narrow', (S, S, S), (1, 2, 2), 'dim', [0]), ('narrow', (S, S, S), (1, 0, 0), 'empty_dim', [0]), ('squeeze', (S, 1, S, 1), NO_ARGS), ('squeeze', (1, 1, 1, 1), NO_ARGS, 'input_sizes_are_ones'), ('squeeze', (S, 1, S, 1), (1,), '1_dim', [0]), ('squeeze', (S, 1, S, 1), (2,), 'not_1_dim', [0]), ('squeeze', (), (0,), 'scalar', [0]), ('unsqueeze', (S, S, S), (0,), 'first', [0]), ('unsqueeze', (S, S, S), (1,), 'middle', [0]), ('unsqueeze', (S, S, S), (3,), 'last', [0]), ('unsqueeze', (), (0,), 'scalar', [0]), ('chunk', (S, S, S), (2,)), ('chunk', (S, S, S), (S, 1), 'dim', [1]), ('split', (S, S, S), (2,)), ('split', (S, S, S), (S, 1), 'dim', [1]), ('split', (S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],), 'size_list'), ('split', (S, S, S), ([int(S / 2), S - int(S / 2) * 2, int(S / 2)], 2), 'size_list_dim', [1]), ('gather', (M, S), (0, gather_variable((S, S), 1, M, True)), 'dim0', [0]), ('gather', (M, S), (1, gather_variable((M, S // 2), 0, S, True)), 'dim1', [0]), ('gather', (), (0, torch.tensor([0], dtype=torch.int64)), 'scalar_input', [0]), ('gather', (S,), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_index', [0]), ('gather', (), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_both', [0]), ('scatter', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', [0]), ('scatter', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', [0]), ('scatter', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalartensor_all_dim0', [0]), ('scatter', (), (0, torch.tensor(0, dtype=torch.int64), 2.5), 'scalar_all_dim0', [0]), ('scatter_add', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', [0]), ('scatter_add', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', [0]), ('scatter_add', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim0', [0]), ('masked_select', (M, M), (mask_not_all_zeros((M, M)),)), ('masked_select', (M, M), (mask_not_all_zeros((M,)),), 'broadcast_rhs'), ('masked_select', (M,), (mask_not_all_zeros((M, M)),), 'broadcast_lhs'), ('masked_select', (M, 1, M), (mask_not_all_zeros((M, M)),), 'broadcast_all'), ('masked_select', (), (torch.tensor(1, dtype=torch.uint8),), 'scalar'), ('masked_select', (M, M), (torch.tensor(1, dtype=torch.uint8),), 'scalar_broadcast_rhs'), ('masked_select', (), (mask_not_all_zeros((M, M)),), 'scalar_broadcast_lhs'), ('masked_fill', (M, M), (torch.ByteTensor(M, M).bernoulli_(), 10)), ('masked_fill', (M, M), (torch.ByteTensor(M, M).bernoulli_(), ()), 'tensor'), ('masked_fill', (M,), (torch.ByteTensor(M, M).bernoulli_(), 10), 'broadcast_lhs'), ('masked_fill', (M, M), (torch.ByteTensor(M,).bernoulli_(), 10), 'broadcast_rhs'), ('masked_fill', (), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), 10), 'scalar'), ('masked_fill', (), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), ()), 'scalar_variable'), ('masked_fill', (M, M), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), 10), 'scalar_broadcast_rhs'), ('masked_scatter', (M, M), (torch.ByteTensor(M, M).bernoulli_(), (M, M))), ('masked_scatter', (M,), (torch.ByteTensor(M, M).bernoulli_(), (M, M)), 'broadcast_lhs'), ('masked_scatter', (M, M), (torch.ByteTensor(M,).bernoulli_(), (M, M)), 'broadcast_rhs'), ('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)), 'scalar'), ('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)), 'scalar_broadcast_rhs'), ('resize_', (S, S, S), (torch.Size([S * S, S])), 'fewer_dims'), ('resize_', (), (dont_convert(()),), 'scalar'), ('resize_', (), (torch.Size([1, 1, 1])), 'scalar_to_dims'), ('resize_as_', (), (non_differentiable(torch.tensor(5.)),), 'scalar'), ('resize_as_', (), (non_differentiable(torch.randn((1, 1, 1))),), 'scalar_to_dims'), ('resize_as_', (S, S, S), (non_differentiable(torch.randn(S * S, S)),)), ('sort', (S, M, S), NO_ARGS), ('sort', (S, M, S), (1,), 'dim'), ('sort', (S, M, S), (1, True), 'dim_desc'), ('sort', (), NO_ARGS, 'scalar'), ('sort', (), (0,), 'dim_scalar'), ('sort', (), (0, True), 'dim_desc_scalar'), ('topk', (S, M, S), (3,)), ('topk', (S, M, S), (3, 1), 'dim', [1]), ('topk', (S, M, S), (3, 1, True), 'dim_desc', [1]), ('topk', (S, M, S), (3, 1, True, True), 'dim_desc_sort', [1]), ('topk', (), (1,), 'scalar'), ('topk', (), (1, 0), 'dim_scalar', [1]), ('topk', (), (1, 0, True), 'dim_desc_scalar', [1]), ('topk', (), (1, 0, True, True), 'dim_desc_sort_scalar', [1]), ('take', (S, S, S), (torch.LongTensor([[-3, 2], [20, 2]]),)), ('take', (S, S, S), (torch.tensor(0, dtype=torch.int64),), 'scalar_index'), ('take', (), (torch.LongTensor([0]),), 'scalar_data'), ('take', (), (torch.tensor(0, dtype=torch.int64),), 'scalar_both'), ('where', (M, M), (mask_not_all_zeros((M, M)), (M, M))), ('where', (M, 1, M), (mask_not_all_zeros((M, M)), (M, M, 1)), 'broadcast_all'), ('where', (), (bernoulli_scalar(), ()), 'scalar'), ('where', (M, 1, M), (bernoulli_scalar(), (M, M, 1)), 'scalar_broadcast_mask'), ('where', (), (mask_not_all_zeros((M, M)), ()), 'scalar_broadcast_non_mask'), ('__getitem__', torch.randn(S, S, S), (dont_convert([1, 2]),)), ('__getitem__', torch.randn(S, S, S), (slice(0, 3),), 'slice'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(0, 3), 1]),), 'slice_index'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3], [0, 0, 2]]),), 'adv_index'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 0, 3], [1, 1, 3], [0, 0, 2]]),), 'adv_index_dup'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), slice(None), [0, 3]]),), 'adv_index_end'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), [0, 3], slice(None)]),), 'adv_index_mid'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None), slice(None)]),), 'adv_index_beg'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], [1, 2], slice(None)]),), 'adv_index_comb'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], ]),), 'adv_index_sub'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None)]),), 'adv_index_sub_2'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], Ellipsis]),), 'adv_index_sub_3'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3], torch.LongTensor([0, 0, 2])]),), 'adv_index_var'), ] # TODO: clamp with min/max def create_input(call_args, requires_grad=True, non_contiguous=False, call_kwargs=None): if not isinstance(call_args, tuple): call_args = (call_args,) def map_arg(arg): def maybe_non_contig(tensor): return tensor if not non_contiguous else make_non_contiguous(tensor) if isinstance(arg, torch.Size) or isinstance(arg, dont_convert): return arg elif isinstance(arg, tuple) and len(arg) == 0: var = torch.randn((), dtype=torch.double) var.requires_grad = requires_grad return var elif isinstance(arg, tuple) and not isinstance(arg[0], torch.Tensor): return Variable(maybe_non_contig(torch.randn(*arg, dtype=torch.double)), requires_grad=requires_grad) elif isinstance(arg, non_differentiable): if isinstance(arg.tensor, torch.Tensor): return maybe_non_contig(arg.tensor) return maybe_non_contig(arg.tensor) elif isinstance(arg, torch.Tensor): if arg.dtype == torch.float: arg = arg.double() # NOTE: We do clone() after detach() here because we need to be able to change size/storage of v afterwards v = maybe_non_contig(arg).detach().clone() v.requires_grad = requires_grad and v.is_floating_point() return v elif callable(arg): return map_arg(arg()) else: return arg args_out = tuple(map_arg(arg) for arg in call_args) kwargs_out = {k: map_arg(v) for k, v in call_kwargs.items()} if call_kwargs else {} return args_out, kwargs_out def _compare_trilu_indices( self, row, col, offset=0, dtype=torch.long, device='cpu'): if row == 0 or col == 0: # have to handle this separately as tril and triu does not take # empty matrix as input self.assertEqual( torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1), torch.tril_indices(row, col, offset, dtype=dtype, device=device)) self.assertEqual( torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1), torch.triu_indices(row, col, offset, dtype=dtype, device=device)) else: self.assertEqual( torch.ones(row, col, dtype=dtype, device='cpu') .tril(offset).nonzero().transpose(0, 1).to(device), torch.tril_indices(row, col, offset, dtype=dtype, device=device)) self.assertEqual( torch.ones(row, col, dtype=dtype, device='cpu') .tril(offset).nonzero().transpose(0, 1).to(device), torch.tril_indices(row, col, offset, dtype=dtype, device=device)) def _compare_large_trilu_indices( self, row, col, offset=0, dtype=torch.long, device='cpu'): l = torch.ones(row, col, dtype=dtype, device='cpu').tril(offset) \ .nonzero()[-100:-1, :].transpose(0, 1).to(device) torch.cuda.empty_cache() r = torch.tril_indices( row, col, offset, dtype=dtype, device=device)[:, -100:-1] self.assertEqual(l, r) torch.cuda.empty_cache() l = torch.ones(row, col, dtype=dtype, device='cpu').triu(offset) \ .nonzero()[-100:-1, :].transpose(0, 1).to(device) torch.cuda.empty_cache() r = torch.triu_indices( row, col, offset, dtype=dtype, device=device)[:, -100:-1] self.assertEqual(l, r) torch.cuda.empty_cache() # ( # row # col # offset (optional) # dtype (optional) # ) tri_tests_args = [ (1, 1), (3, 3), (3, 3, 1), (3, 3, 2), (3, 3, 200), (3, 3, -1), (3, 3, -2), (3, 3, -200), (0, 3, 0), (0, 3, 1), (0, 3, -1), (3, 0, 0), (3, 0, 1), (3, 0, -1), (0, 0, 0), (0, 0, 1), (0, 0, -1), (3, 6, 0), (3, 6, 1), (3, 6, 3), (3, 6, 9), (3, 6, -1), (3, 6, -3), (3, 6, -9), (6, 3, 0), (6, 3, 1), (6, 3, 3), (6, 3, 9), (6, 3, -1), (6, 3, -3), (6, 3, -9), (258, 253, 1, torch.float32), (257, 258, 1, torch.float64), (258, 258, 1, torch.short), (3, 513, 1, torch.long), (513, 3, 1, torch.int), (513, 0, 1, torch.double), (1024, 1024), (1024, 1024, 500, torch.float32), (1024, 1024, 1023), (1024, 1024, -500), (1023, 1025), (1025, 1023, 1022), (1024, 1024, -500), (3, 2028), (3, 2028, 1), (3, 2028, -1), (2028, 3), (2028, 1), (2028, 1, -1) ] tri_large_tests_args = [ # Large test cases below are deliberately commented out to speed up CI # tests and to avoid OOM error. When modifying implementations of # tril_indices and triu_indices, please enable these tests and make sure # they pass. # # (1, 268435455), # (5000, 5000), # (10000, 10000), # (268435455, 1), # (134217727, 2, 1), # (2, 134217727, 1), # (536870901, 1), # (1, 536870901), # (268435455, 2, 1), # (2, 268435455, 1) ] def run_additional_tri_tests(self, device): x = torch.ones( 3, 3, dtype=torch.long, device=device, layout=torch.strided) l = x.tril(0).nonzero().transpose(0, 1) u = x.triu(0).nonzero().transpose(0, 1) self.assertEqual(l, torch.tril_indices(3, 3, device=device)) self.assertEqual( l, torch.tril_indices(3, 3, device=device, layout=torch.strided)) self.assertEqual(u, torch.triu_indices(3, 3, device=device)) self.assertEqual( u, torch.triu_indices(3, 3, device=device, layout=torch.strided)) self.assertRaises( RuntimeError, lambda: torch.triu_indices( 1, 1, device=device, layout=torch.sparse_coo)) self.assertRaises( RuntimeError, lambda: torch.tril_indices( 1, 1, device=device, layout=torch.sparse_coo)) def unpack_variables(args): if istuple(args): return tuple(unpack_variables(elem) for elem in args) else: return args EXCLUDE_FUNCTIONAL = { 'addmm', 'addmm_', 'addbmm', 'baddbmm', 'addmv', 'addmv_', 'addr', 'addr_', 'reshape', 'where' # argument order } EXCLUDE_GRADCHECK = { } EXCLUDE_GRADGRADCHECK = { } EXCLUDE_GRADGRADCHECK_BY_TEST_NAME = { # *det methods uses svd in backward when matrix is not invertible. However, # svd backward is unstable unless the matrix has positive distinct singular # values. Generated random matrices satisfy this with high probability, but # we can't rely on it. So only test gradgrad on invertible test cases and # _distinct_singular_values. 'test_det', 'test_det_1x1', 'test_det_symmetric', 'test_det_symmetric_psd', 'test_det_dim2_null', 'test_det_rank1', 'test_det_rank2', # `other` expand_as(self, other) is not used in autograd. 'test_expand_as', 'test_logdet', 'test_logdet_1x1', 'test_logdet_symmetric', 'test_slogdet_1x1_neg_det', 'test_slogdet_neg_det', 'test_slogdet_symmetric', 'test_cdist', } def exclude_tensor_method(name, test_name): # there are no tensor equivalents for these (inplace or out) exclude_all_tensor_method_by_test_name = { 'test_clamp_min', 'test_clamp_max', 'test_clamp_min_scalar', 'test_clamp_max_scalar', 'test_slice', 'test_where', 'test_where_broadcast_all', 'test_where_scalar', 'test_where_scalar_broadcast_mask', 'test_where_scalar_broadcast_non_mask', } # there are no out-of-place tensor equivalents for these exclude_outplace_tensor_method = { 'index_add', 'index_copy', 'index_fill', 'masked_fill', 'masked_scatter', 'scatter', 'scatter_add', 'det', } if test_name in exclude_all_tensor_method_by_test_name: return True is_magic_method = name[:2] == '__' and name[-2:] == '__' is_inplace = name[-1] == "_" and not is_magic_method if not is_inplace and name in exclude_outplace_tensor_method: return True return False