| import torch |
| from torch._six import inf, nan |
| from functools import reduce, wraps |
| from operator import mul, itemgetter |
| from torch.autograd import Variable, Function, detect_anomaly |
| 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) |
| |
| |
| 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 |
| |
| |
| class non_differentiable(object): |
| def __init__(self, tensor): |
| self.tensor = 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 |
| # ) |
| method_tests = [ |
| ('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'), |
| ('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'), |
| ('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)), |
| ('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]), |
| ('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'), |
| ('triu', (M, M), NO_ARGS), |
| ('triu', (M, M), (2,), 'idx'), |
| ('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), torch.tensor([2.])), 'scalar_input_dim', [0]), |
| ('index_add', (), (0, torch.tensor(0, dtype=torch.int64), torch.tensor(2.)), '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), torch.tensor([2.])), 'scalar_input_dim', [0]), |
| ('index_copy', (), (0, torch.tensor(0, dtype=torch.int64), torch.tensor(2.)), 'scalar_all_dim', [0]), |
| ('index_fill', (S, S), (0, index_variable(2, S), 2), 'dim', [0]), |
| # FIXME: we should compute the derivative w.r.t torch.tensor(2) |
| ('index_fill', (S, S), (0, index_variable(2, S), non_differentiable(torch.tensor(2))), |
| '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]), |
| ('gesv', (S, S), (random_fullrank_matrix_distinct_singular_value(S, silent=True),), '', NO_ARGS, [skipIfNoLapack]), |
| ('gesv', (S, S, S), (random_fullrank_matrix_distinct_singular_value(S, S, silent=True),), |
| 'batched', NO_ARGS, [skipIfNoLapack]), |
| ('gesv', (2, 3, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 3, silent=True),), |
| 'batched_dims', NO_ARGS, [skipIfNoLapack]), |
| ('gesv', (2, 2, S, S), (random_fullrank_matrix_distinct_singular_value(S, 1, silent=True),), |
| 'batched_broadcast_A', NO_ARGS, [skipIfNoLapack]), |
| ('gesv', (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'), |
| # FIXME: we should compute the derivative w.r.t torch.tensor(1) |
| ('fill_', (S, S, S), (non_differentiable(torch.tensor(1)),), '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), ()), '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_(), torch.tensor(10)), 'tensor'), |
| # no lhs or all broadcast on masked_fill or masked_scatter because it's always inplace |
| ('masked_fill', (M, M), (torch.ByteTensor(M,).bernoulli_(), 10), 'broadcast_rhs'), |
| ('masked_fill', (), (torch.tensor(0, dtype=torch.uint8, requires_grad=False).bernoulli_(), 10), 'scalar'), |
| ('masked_fill', (), (torch.tensor(0, dtype=torch.uint8, requires_grad=False).bernoulli_(), torch.tensor(10)), |
| 'scalar_variable'), |
| ('masked_fill', (M, M), (torch.tensor(0, dtype=torch.uint8, requires_grad=False).bernoulli_(), 10), |
| 'scalar_broadcast_rhs'), |
| ('masked_scatter', (M, M), (torch.ByteTensor(M, M).bernoulli_(), (M, M))), |
| ('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() |
| v = maybe_non_contig(arg).detach() |
| 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 unpack_variables(args): |
| if isinstance(args, tuple): |
| 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', |
| 'test_logdet', |
| 'test_logdet_1x1', |
| 'test_logdet_symmetric', |
| 'test_slogdet_1x1_neg_det', |
| 'test_slogdet_neg_det', |
| 'test_slogdet_symmetric', |
| } |
| |
| |
| 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 |
