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common_methods_invocations.py
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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, set_rng_seed)
def index_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.rand(*shape).mul_(max_indices).floor_().long()
return index
def index_perm_variable(shape, max_indices):
if not isinstance(shape, tuple):
shape = (shape,)
index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape)
return index
def gather_variable(shape, index_dim, max_indices, duplicate=False):
assert len(shape) == 2
assert index_dim < 2
batch_dim = 1 - index_dim
index = torch.LongTensor(*shape)
for i in range(shape[index_dim]):
index.select(index_dim, i).copy_(
torch.randperm(max_indices)[:shape[batch_dim]])
if duplicate:
index.select(batch_dim, 0).copy_(index.select(batch_dim, 1))
return index
def bernoulli_scalar():
return torch.tensor(0, dtype=torch.uint8).bernoulli_()
def mask_not_all_zeros(shape):
assert len(shape) > 0
while True:
result = torch.randn(shape).gt(0)
if result.sum() > 0:
return result
def uniform_scalar(offset=0, requires_grad=False):
v = torch.rand(()) + offset
v.requires_grad = requires_grad
return v
def normal_scalar_clamp(amin, amax, requires_grad=False):
v = torch.randn(()).clamp(amin, amax)
v.requires_grad = requires_grad
return v
def prod_zeros(dim_size, dim_select):
assert len(dim_select) == 2
result = torch.randn(dim_size, dim_size, dim_size)
result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_()
result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_()
result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_()
return result
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
# )
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
def create_input(call_args, requires_grad=True, non_contiguous=False, call_kwargs=None):
if not isinstance(call_args, tuple):
call_args = (call_args,)
def map_arg(arg):
def maybe_non_contig(tensor):
return tensor if not non_contiguous else make_non_contiguous(tensor)
if isinstance(arg, torch.Size) or isinstance(arg, dont_convert):
return arg
elif isinstance(arg, tuple) and len(arg) == 0:
var = torch.randn((), dtype=torch.double)
var.requires_grad = requires_grad
return var
elif isinstance(arg, tuple) and not isinstance(arg[0], torch.Tensor):
return Variable(maybe_non_contig(torch.randn(*arg, dtype=torch.double)), requires_grad=requires_grad)
elif isinstance(arg, non_differentiable):
if isinstance(arg.tensor, torch.Tensor):
return maybe_non_contig(arg.tensor)
return maybe_non_contig(arg.tensor)
elif isinstance(arg, torch.Tensor):
if arg.dtype == torch.float:
arg = arg.double()
# NOTE: We do clone() after detach() here because we need to be able to change size/storage of v afterwards
v = maybe_non_contig(arg).detach().clone()
v.requires_grad = requires_grad and v.is_floating_point()
return v
elif callable(arg):
return map_arg(arg())
else:
return arg
args_out = tuple(map_arg(arg) for arg in call_args)
kwargs_out = {k: map_arg(v) for k, v in call_kwargs.items()} if call_kwargs else {}
return args_out, kwargs_out
def _compare_trilu_indices(
self, row, col, offset=0, dtype=torch.long, device='cpu'):
if row == 0 or col == 0:
# have to handle this separately as tril and triu does not take
# empty matrix as input
self.assertEqual(
torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1),
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
self.assertEqual(
torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1),
torch.triu_indices(row, col, offset, dtype=dtype, device=device))
else:
self.assertEqual(
torch.ones(row, col, dtype=dtype, device='cpu')
.tril(offset).nonzero().transpose(0, 1).to(device),
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
self.assertEqual(
torch.ones(row, col, dtype=dtype, device='cpu')
.tril(offset).nonzero().transpose(0, 1).to(device),
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
def _compare_large_trilu_indices(
self, row, col, offset=0, dtype=torch.long, device='cpu'):
l = torch.ones(row, col, dtype=dtype, device='cpu').tril(offset) \
.nonzero()[-100:-1, :].transpose(0, 1).to(device)
torch.cuda.empty_cache()
r = torch.tril_indices(
row, col, offset, dtype=dtype, device=device)[:, -100:-1]
self.assertEqual(l, r)
torch.cuda.empty_cache()
l = torch.ones(row, col, dtype=dtype, device='cpu').triu(offset) \
.nonzero()[-100:-1, :].transpose(0, 1).to(device)
torch.cuda.empty_cache()
r = torch.triu_indices(
row, col, offset, dtype=dtype, device=device)[:, -100:-1]
self.assertEqual(l, r)
torch.cuda.empty_cache()
# (
# row
# col
# offset (optional)
# dtype (optional)
# )
tri_tests_args = [
(1, 1),
(3, 3),
(3, 3, 1),
(3, 3, 2),
(3, 3, 200),
(3, 3, -1),
(3, 3, -2),
(3, 3, -200),
(0, 3, 0),
(0, 3, 1),
(0, 3, -1),
(3, 0, 0),
(3, 0, 1),
(3, 0, -1),
(0, 0, 0),
(0, 0, 1),
(0, 0, -1),
(3, 6, 0),
(3, 6, 1),
(3, 6, 3),
(3, 6, 9),
(3, 6, -1),
(3, 6, -3),
(3, 6, -9),
(6, 3, 0),
(6, 3, 1),
(6, 3, 3),
(6, 3, 9),
(6, 3, -1),
(6, 3, -3),
(6, 3, -9),
(258, 253, 1, torch.float32),
(257, 258, 1, torch.float64),
(258, 258, 1, torch.short),
(3, 513, 1, torch.long),
(513, 3, 1, torch.int),
(513, 0, 1, torch.double),
(1024, 1024),
(1024, 1024, 500, torch.float32),
(1024, 1024, 1023),
(1024, 1024, -500),
(1023, 1025),
(1025, 1023, 1022),
(1024, 1024, -500),
(3, 2028),
(3, 2028, 1),
(3, 2028, -1),
(2028, 3),
(2028, 1),
(2028, 1, -1)
]
tri_large_tests_args = [
(1, 268435455),
# Large test cases below are deliberately commented out to speed up CI
# tests and to avoid OOM error. When modifying implementations of
# tril_indices and triu_indices, please enable these tests and make sure
# they pass.
#
# (5000, 5000),
# (10000, 10000),
# (268435455, 1),
# (134217727, 2, 1),
# (2, 134217727, 1),
# (536870901, 1),
# (1, 536870901),
# (268435455, 2, 1),
# (2, 268435455, 1)
]
def run_additional_tri_tests(self, device):
x = torch.ones(
3, 3, dtype=torch.long, device=device, layout=torch.strided)
l = x.tril(0).nonzero().transpose(0, 1)
u = x.triu(0).nonzero().transpose(0, 1)
self.assertEqual(l, torch.tril_indices(3, 3, device=device))
self.assertEqual(
l, torch.tril_indices(3, 3, device=device, layout=torch.strided))
self.assertEqual(u, torch.triu_indices(3, 3, device=device))
self.assertEqual(
u, torch.triu_indices(3, 3, device=device, layout=torch.strided))
self.assertRaises(
RuntimeError,
lambda: torch.triu_indices(
1, 1, device=device, layout=torch.sparse_coo))
self.assertRaises(
RuntimeError,
lambda: torch.tril_indices(
1, 1, device=device, layout=torch.sparse_coo))
def unpack_variables(args):
if 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',