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)
+ random_fullrank_matrix_distinct_singular_value, set_rng_seed)
def index_variable(shape, max_indices):
M = 10
S = 5
+
# (
# method name,
# input size/constructing fn,
# 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'),
- ('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'),
- ('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]),
- ('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'),
- ('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'),
-]
+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'),
+ ('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]),
+ ('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'),
+ ('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
projects / platform / upstream / pytorch.git / commitdiff