mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
a21e256e8d
Summary: Fixes #17962 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18633 Differential Revision: D14700449 Pulled By: wanchaol fbshipit-source-id: 3d15d67c01b69b28394a0f2f001db90ed9fd31dc
1080 lines
57 KiB
Python
1080 lines
57 KiB
Python
import torch
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from torch._six import inf, istuple
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from functools import reduce
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from operator import mul, itemgetter
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import collections
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from torch.autograd import Variable
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from torch.testing import make_non_contiguous
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from common_utils import (skipIfNoLapack,
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prod_single_zero, random_square_matrix_of_rank,
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random_symmetric_matrix, random_symmetric_psd_matrix,
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random_symmetric_pd_matrix, make_nonzero_det,
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random_fullrank_matrix_distinct_singular_value, set_rng_seed)
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def index_variable(shape, max_indices):
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if not isinstance(shape, tuple):
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shape = (shape,)
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index = torch.rand(*shape).mul_(max_indices).floor_().long()
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return index
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def index_perm_variable(shape, max_indices):
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if not isinstance(shape, tuple):
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shape = (shape,)
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index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape)
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return index
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def gather_variable(shape, index_dim, max_indices, duplicate=False):
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assert len(shape) == 2
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assert index_dim < 2
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batch_dim = 1 - index_dim
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index = torch.LongTensor(*shape)
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for i in range(shape[index_dim]):
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index.select(index_dim, i).copy_(
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torch.randperm(max_indices)[:shape[batch_dim]])
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if duplicate:
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index.select(batch_dim, 0).copy_(index.select(batch_dim, 1))
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return index
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def bernoulli_scalar():
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return torch.tensor(0, dtype=torch.uint8).bernoulli_()
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def mask_not_all_zeros(shape):
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assert len(shape) > 0
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while True:
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result = torch.randn(shape).gt(0)
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if result.sum() > 0:
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return result
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def uniform_scalar(offset=0, requires_grad=False):
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v = torch.rand(()) + offset
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v.requires_grad = requires_grad
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return v
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def normal_scalar_clamp(amin, amax, requires_grad=False):
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v = torch.randn(()).clamp(amin, amax)
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v.requires_grad = requires_grad
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return v
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def prod_zeros(dim_size, dim_select):
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assert len(dim_select) == 2
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result = torch.randn(dim_size, dim_size, dim_size)
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result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_()
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result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_()
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result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_()
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return result
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non_differentiable = collections.namedtuple('non_differentiable', ['tensor'])
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class dont_convert(tuple):
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pass
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class NoArgsClass(object):
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def __iter__(self):
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return self
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def __next__(self):
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raise StopIteration()
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next = __next__ # Python 2 compatibility
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def __len__(self):
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return 0
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NO_ARGS = NoArgsClass()
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L = 20
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M = 10
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S = 5
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# (
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# method name,
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# input size/constructing fn,
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# args (tuple represents shape of a tensor arg),
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# test variant name (will be used at test name suffix), // optional
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# (True, nonfusible_nodes, fusible_nodes) for autodiff, // optional
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# indices for possible dim arg, // optional
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# fn mapping output to part that should be gradcheck'ed, // optional
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# )
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# Note: some functions have separate schema for (Tensor other) and (Scalar other),
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# and it's possible that we only support AD for Scalar version but not Tensor
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# version, and vice versa.
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# When writing tests, only scalar(float/int) input triggers the Scalar schema.
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# uniform_scalar produces a scalar **Tensor** which won't match Scalar input.
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def method_tests():
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set_rng_seed(0)
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return [
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('add', (S, S, S), ((S, S, S),), '', (True,)),
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('add', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
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('add', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
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('add', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
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('add', (), ((),), 'scalar', (True,)),
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('add', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
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('add', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
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('add', (S, S, S), (3.14,), 'constant', (True,)),
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('add', (), (3.14,), 'scalar_constant', (True,)),
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('__radd__', (S, S, S), (3.14,), 'constant', (True, 'aten::add')),
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('__radd__', (), (3.14,), 'scalar_constant', (True, 'aten::add')),
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('sub', (S, S, S), ((S, S, S),), '', (True,)),
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('sub', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
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('sub', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
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('sub', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
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('sub', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
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('sub', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
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('sub', (S, S, S), (3.14,), 'constant', (True,)),
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('sub', (), (3.14,), 'scalar_constant', (True,)),
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('__rsub__', (S, S, S), (3.14,), 'constant', (True, 'aten::rsub')),
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('__rsub__', (), (3.14,), 'scalar_constant', (True, 'aten::rsub')),
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('mul', (S, S, S), ((S, S, S),), '', (True,)),
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('mul', (), ((),), 'scalar', (True,)),
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('mul', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)),
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('mul', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)),
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('mul', (S, 1, S), ((M, S),), 'broadcast_all', (True,)),
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('mul', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)),
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('mul', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
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('mul', (S, S, S), (3.14,), 'constant', (True,)),
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('mul', (), (3.14,), 'scalar_constant', (True,)),
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('__rmul__', (S, S, S), (3.14,), 'constant', (True, 'aten::mul')),
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('__rmul__', (), (3.14,), 'scalar_constant', (True, 'aten::mul')),
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('div', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)),
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('div', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs', (True,)),
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('div', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)),
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('div', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all', (True,)),
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('div', (), (uniform_scalar(0.1),), 'scalar', (True,)),
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('div', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
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('div', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)),
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('div', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)),
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('__rdiv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant',
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(True, [], ['aten::mul', 'aten::reciprocal'])),
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('div', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
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('__rdiv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant',
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(True, [], ['aten::mul', 'aten::reciprocal'])),
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('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(S, S, S) + 0.1,), '', (True,)),
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('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(1,) + 0.1,), 'broadcast_rhs', (True,)),
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('pow', torch.rand(1,) + 1e-3, (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)),
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('pow', torch.rand(S, 1, S) + 1e-3, (torch.rand(1, S, 1) + 0.1,), 'broadcast_all', (True,)),
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('pow', uniform_scalar(1e-3, requires_grad=True), (uniform_scalar(0.1),), 'scalar', (True,)),
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('pow', torch.rand(S, S, S) + 1e-3, (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
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('pow', uniform_scalar(1e-3, requires_grad=True), (torch.rand(S, S, S) + 0.1,), 'scalar_broadcast_lhs', (True,)),
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('pow', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True,)),
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('__rpow__', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True, 'aten::pow')),
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('pow', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
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('__rpow__', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True, 'aten::pow')),
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('transpose', (1, 2, 3), (1, 2), 'dim', (True,), [0, 1]),
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('transpose', (), (0, 0), 'scalar', (True,)),
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('transpose', (1,), (0, 0), '1d', (True,)),
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('transpose', torch.rand(L, L), (0, 1), '2d', (True,)),
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('transpose', torch.rand(S, S, S), (2, 0), '3d', (True,)),
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('t', (1, 2), NO_ARGS, '', (True,)),
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('view', (S, S, S), (S * S, S), '', (True,)),
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('view', (S, S, S), (torch.Size([S * S, S]),), 'size', (True,)),
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('view', (S,), (S,), '1d', (True,)),
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('view', (), (dont_convert(()),), 'scalar_to_scalar', (True,)),
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('view', (), (1,), 'scalar_to_1d', (True,)),
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('reshape', (S, S, S), (S * S, S),),
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('reshape', (S, S, S), (torch.Size([S * S, S]),), 'size'),
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('reshape', (S,), (S,), '1d'),
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('reshape', (), (dont_convert(()),), 'scalar_to_scalar'),
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('reshape', (), (1,), 'scalar_to_1d'),
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('reshape_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)),
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('reshape_as', (), (non_differentiable(torch.tensor(42.)),), 'scalar'),
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('reshape_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'),
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('flip', (S, S, S), ([0],), 'd0'),
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('flip', (S, S, S), ([0, 1, 2],), 'd012'),
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('flip', (S, S, S), ([0, 2],), 'd02'),
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('flip', (S, S, S), ([2, 0],), 'd20'),
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('flip', (S, S, S), ([-1],), 'neg_d'),
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('roll', (S, S, S), (0, 0), 'd0'),
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('roll', (S, S, S), (1, 2), 'd12'),
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('roll', (S, S, S), (0, 2,), 'd02'),
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('roll', (S, S, S), (2, 0,), 'd20'),
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('roll', (S, S, S), (-1, 0), 'neg_shift'),
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('roll', (S, S, S), (10000, 1), 'loop_shift'),
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('roll', (S, S, S), (2,), 'flattened'),
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('roll', (S, S, S), ([1, 2, -1], [0, 1, 2]), 'three_dims'),
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('rot90', (S, S, S), (1, [0, 1],), 'k1_d01'),
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('rot90', (S, S, S), (1, [1, 2],), 'k1_d12'),
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('rot90', (S, S, S), (1, [1, -1],), 'k1_neg_d'),
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('rot90', (S, S, S), (), 'default'),
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('view_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)),
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('view_as', (), (non_differentiable(torch.tensor(5.5)),), 'scalar'),
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('view_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'),
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('expand', (S, 1, 1), (S, S, S), '', (True,)),
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('expand', (torch.Size([S, 1, S]),), (S, S, S), 'size', (True,)),
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('expand', (S, 1), (S, S, S), 'new_dim', (True,)),
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('expand', (1,), (S, S, S), '1_element', (True,)),
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('expand', (1, S), (1, 1, S), 'new_dim_front_old_front_1', (True,)),
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('expand', (), (dont_convert(()),), 'scalar_to_scalar', (True,)),
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('expand', (), (1, 3, 2), 'scalar_to_dims', (True,)),
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('expand_as', (S, 1, 1), (torch.rand(S, S, S),), '', (True,)),
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('exp', (S, S, S), NO_ARGS, '', (True,)),
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('exp', (), NO_ARGS, 'scalar', (True,)),
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('expm1', (S, S, S), NO_ARGS, '', (True,)),
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('expm1', (), NO_ARGS, 'scalar', (True,)),
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('erf', torch.rand(S, S, S), NO_ARGS, '', (True,)),
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('erf', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('erfc', torch.rand(S, S, S), NO_ARGS, '', (True,)),
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('erfc', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('erfinv', torch.rand(S, S, S).clamp(-0.9, 0.9), NO_ARGS),
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('erfinv', normal_scalar_clamp(-0.9, 0.9, requires_grad=True), NO_ARGS, 'scalar'),
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('log', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
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('log', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('log10', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
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('log10', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('log1p', torch.rand(S, S, S), NO_ARGS, '', (True,)),
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('log1p', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('log2', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
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('log2', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('tanh', (S, S, S), NO_ARGS, '', (True,)),
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('tanh', (), NO_ARGS, 'scalar', (True,)),
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('sigmoid', (S, S, S), NO_ARGS, '', (True,)),
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('sigmoid', (), NO_ARGS, 'scalar', (True,)),
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('sinh', (S, S, S), NO_ARGS, '', (True,)),
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('sinh', (), NO_ARGS, 'scalar', (True,)),
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('cosh', (S, S, S), NO_ARGS, '', (True,)),
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('cosh', (), NO_ARGS, 'scalar', (True,)),
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('abs', (S, S, S), NO_ARGS, '', (True,)),
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('abs', (), NO_ARGS, 'scalar', (True,)),
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('clamp', (S, S, S), (0, 1), '', (True,)),
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('clamp', (S, S, S), (None, 0.5), 'min', (True,)),
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('clamp', (S, S, S), (0.5, None), 'max', (True,)),
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('clamp', (), (0, 1), 'scalar', (True,)),
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('clamp', (), (None, 0.5), 'min_scalar', (True,)),
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('clamp', (), (0.5, None), 'max_scalar', (True,)),
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('sqrt', torch.rand(S, S, S) + 5e-4, NO_ARGS, '', (True,)),
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('sqrt', uniform_scalar(5e-4, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('sin', (S, S, S), NO_ARGS, '', (True,)),
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('sin', (), NO_ARGS, 'scalar', (True,)),
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('cos', (S, S, S), NO_ARGS, '', (True,)),
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('cos', (), NO_ARGS, 'scalar', (True,)),
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('tan', torch.randn(S, S, S).clamp(-1, 1), NO_ARGS, '', (True,)),
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('asin', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
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('acos', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
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('atan', (S, S, S), NO_ARGS, '', (True,)),
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('atan', (), NO_ARGS, 'scalar', (True,)),
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('atan2', (S, S, S), ((S, S, S),)),
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('atan2', (), ((),), 'scalar'),
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('atan2', (S, S, S), ((S,),), 'broadcast_rhs'),
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('atan2', (S,), ((S, S, S),), 'broadcast_lhs'),
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('atan2', (S, 1, S), ((S, S),), 'broadcast_all'),
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('reciprocal', torch.rand(S, S, S) + 0.1, NO_ARGS, '', (True,)),
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('reciprocal', uniform_scalar(0.1, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('round', (S, S, S), NO_ARGS, '', (True,)),
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('round', (), NO_ARGS, 'scalar', (True,)),
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('sign', (S, S, S), NO_ARGS),
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('sign', (), NO_ARGS, 'scalar'),
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('trunc', (S, S, S), NO_ARGS, '', (True,)),
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('trunc', (), NO_ARGS, 'scalar', (True,)),
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('floor', (S, S, S), NO_ARGS, '', (True,)),
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('floor', (), NO_ARGS, 'scalar', (True,)),
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('ceil', (S, S, S), NO_ARGS, '', (True,)),
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('ceil', (), NO_ARGS, 'scalar', (True,)),
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('rsqrt', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
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('rsqrt', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
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('frac', (S, S, S), NO_ARGS, '', (True,)),
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('frac', (), NO_ARGS, 'scalar', (True,)),
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('fmod', (S, S, S), (1.5,), '', (True,)),
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('fmod', (), (1.5,), 'scalar', (True,)),
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('fmod', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
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('fmod', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
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('fmod', (S, S, S), (non_differentiable(torch.rand(S) + 1.5),), 'tensor_broadcast_rhs'),
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('fmod', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
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('fmod', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
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('fmod', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
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('fmod', (S, S, S), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor_broadcast_rhs'),
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('remainder', (S, S, S), (1.5,), '', (True,)),
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('remainder', (), (1.5,), 'scalar', (True,)),
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('remainder', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
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('remainder', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
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('remainder', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
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('remainder', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
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('remainder', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
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('lerp', (S, S, S), ((S, S, S), 0.4), 'scalar_no_broadcast', (True,)),
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('lerp', (S, S, S), ((S,), 0.4), 'broadcast_rhs', (True,)),
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('lerp', (S,), ((S, S, S), 0.4), 'broadcast_lhs', (True,)),
|
|
('lerp', (S, 1, S), ((S, S), 0.4), 'broadcast_all', (True,)),
|
|
('lerp', (), ((), 0.4), 'scalar', (True,)),
|
|
('lerp', (S, S, S), ((), 0.4), 'scalar_broadcast_rhs', (True,)),
|
|
('lerp', (), ((S, S, S), 0.4), 'scalar_broadcast_lhs', (True,)),
|
|
('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', (True,)),
|
|
('max', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)),
|
|
('max', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)),
|
|
('max', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)),
|
|
('max', (), ((),), 'scalar_elementwise', (True,)),
|
|
('max', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)),
|
|
('max', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)),
|
|
('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', (True,)),
|
|
('min', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)),
|
|
('min', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)),
|
|
('min', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)),
|
|
('min', (), ((),), 'scalar_elementwise', (True,)),
|
|
('min', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)),
|
|
('min', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)),
|
|
('mean', (S, S, S), NO_ARGS, '', (True,)),
|
|
('mean', (S, S, S), (1,), 'dim', (True,), [0]),
|
|
('mean', (S, S, S), (1, True,), 'keepdim_dim', (True,), [0]),
|
|
('mean', (), NO_ARGS, 'scalar', (True,)),
|
|
('mean', (), (0,), 'scalar_dim', (True,), [0]),
|
|
('mean', (), (0, True,), 'scalar_keepdim_dim', (True,), [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, '', (True,)),
|
|
('var', (S, S, S), (1,), 'dim', (True,), [0]),
|
|
('var', (S, S, S), (1, True, True), 'keepdim_dim', (True,), [0]),
|
|
('var', (S,), (0,), 'dim_1d', (True,), [0]),
|
|
('var', (S,), (0, True, True), 'keepdim_dim_1d', (True,), [0]),
|
|
('std', (S, S, S), NO_ARGS, '', (True,)),
|
|
('std', (S, S, S), (1,), 'dim', (True,), [0]),
|
|
('std', (S, S, S), (1, True, True), 'keepdim_dim', (True,), [0]),
|
|
('std', (S,), (0,), 'dim_1d', (True,), [0]),
|
|
('std', (S,), (0, True, True), 'keepdim_dim_1d', (True,), [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)), '', (True, ['aten::add', 'aten::mm'])),
|
|
('addmm', (1,), ((S, S), (S, M)), 'broadcast_lhs', (True, ['aten::add', 'aten::mm'])),
|
|
('addmm', (S, M), ((S, S), (S, M)), 'coef', (True,), (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}),
|
|
('addmm', (1,), ((S, S), (S, M)), 'broadcast_lhs_coef', (True,), (), (), lambda x: x, {'beta': 0.2, 'alpha': 0.6}),
|
|
('addmm', (), ((S, S), (S, M)), 'scalar_broadcast_lhs', (True, ['aten::add', 'aten::mm'])),
|
|
('addmm', (), ((S, S), (S, M)), 'scalar_broadcast_lhs_coef', (True,), (), (), 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,),), '', (True,)),
|
|
('mm', (S, M), ((M, S),), '', (True,)),
|
|
('bmm', (M, S, M), ((M, M, S),), '', (True,)),
|
|
('mv', (S, M), ((M,),), '', (True,)),
|
|
('ger', (S,), ((M,),)),
|
|
('matmul', (L,), ((L,),), '', (True,)),
|
|
('matmul', (S, M), ((M,),), "2d_1d", (True,)),
|
|
('matmul', (M, ), ((M, S),), "1d_2d", (True,)),
|
|
('matmul', (S, M), ((M, S),), "2d_2d", (True,)),
|
|
('matmul', (S, S, M, M), ((S, S, M, S),), "4d_4d", (True,)),
|
|
('matmul', (S, S, M, M), ((M,),), "4d_1d", (True,)),
|
|
('matmul', (M,), ((S, S, M, S),), "1d_4d", (True,)),
|
|
('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)), '', (True,)),
|
|
('addcmul', (S, S), ((S, 1), (1, S)), 'broadcast_rhs', (True,)),
|
|
('addcmul', (1,), ((S, S, 1), (1, S)), 'broadcast_all', (True,)),
|
|
('addcmul', (S, S), ((S, S), (S, S)), 'scale', (True,), (), (), lambda x: x, {'value': 0.5}),
|
|
('addcmul', (S, S), ((S, 1), (1, S)), 'scale_broadcast_rhs', (True,), (), (), lambda x: x, {'value': 0.5}),
|
|
('addcmul', (1,), ((S, S, 1), (1, S)), 'scale_broadcast_all', (True,), (), (), lambda x: x, {'value': 0.5}),
|
|
('addcmul', (), ((), ()), 'scalar', (True,)),
|
|
('addcmul', (S, S), ((), ()), 'scalar_broadcast_rhs', (True,)),
|
|
('addcmul', (), ((S, S, 1), (1, S)), 'scalar_broadcast_lhs', (True,)),
|
|
('addcmul', (), ((), ()), 'scalar_scale', (True,), (), (), lambda x: x, {'value': 0.5}),
|
|
('addcmul', (S, S), ((), ()), 'scalar_scale_broadcast_rhs', (True,), (), (), lambda x: x, {'value': 0.5}),
|
|
('addcmul', (), ((S, S, 1), (1, S)), 'scalar_scale_broadcast_lhs', (True,), (), (), 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,), '', (True,)),
|
|
('logsumexp', (), (0,), 'scalar', (True,)),
|
|
('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'),
|
|
('contiguous', (S, S), NO_ARGS, '', (True,)),
|
|
('contiguous', torch.randn(S, S).transpose(0, 1), NO_ARGS, 'not_contiguous', (True,)),
|
|
('dist', (S, S, S), ((S, S, S),)),
|
|
('dist', (S, S, S), ((S,),), 'broadcast_rhs'),
|
|
('dist', (S,), ((S, S, S),), 'broadcast_lhs'),
|
|
('dist', (S, 1, S), ((S, S),), 'broadcast_all'),
|
|
('dist', (), ((),), 'scalar'),
|
|
('dist', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('dist', (), ((S, S, S),), 'scalar_broadcast_lhs'),
|
|
('dist', (S, S, S), ((S, S, S), 4), '4'),
|
|
('dist', (S, S, S), ((S,), 4), '4_broadcast_rhs'),
|
|
('dist', (S,), ((S, S, S), 4), '4_broadcast_lhs'),
|
|
('dist', (S, 1, S), ((S, S), 4), '4_broadcast_all'),
|
|
('dist', (), ((), 4), 'scalar_4'),
|
|
('dist', (S, S, S), ((), 4), 'scalar_4_broadcast_rhs'),
|
|
('dist', (), ((S, S, S), 4), 'scalar_4_broadcast_lhs'),
|
|
('diag', (M, M), NO_ARGS, '2d'),
|
|
('diag', (3, 5), NO_ARGS, '2d_wide'),
|
|
('diag', (3, 5), (2,), '2d_wide_pos'),
|
|
('diag', (3, 5), (-2,), '2d_wide_neg'),
|
|
('diag', (5, 3), NO_ARGS, '2d_tall'),
|
|
('diag', (5, 3), (2,), '2d_tall_pos'),
|
|
('diag', (5, 3), (-2,), '2d_tall_neg'),
|
|
('diag', (M,), NO_ARGS, '1d'),
|
|
('diag', (M, M), (1,), '2d_1'),
|
|
('diag', (M, M), (2,), '2d_2'),
|
|
('diag_embed', (S, S), NO_ARGS),
|
|
('diagonal', (M, M), NO_ARGS, '2d'),
|
|
('diagonal', (3, 5), NO_ARGS, '2d_wide'),
|
|
('diagonal', (3, 5), (2,), '2d_wide_pos'),
|
|
('diagonal', (3, 5), (-2,), '2d_wide_neg'),
|
|
('diagonal', (5, 3), NO_ARGS, '2d_tall'),
|
|
('diagonal', (5, 3), (2,), '2d_tall_pos'),
|
|
('diagonal', (5, 3), (-2,), '2d_tall_neg'),
|
|
('diagonal', (M, M), (1,), '2d_1'),
|
|
('diagonal', (M, M), (2,), '2d_2'),
|
|
('diagonal', (M, M, M), (1, 1, 2), '3d_1'),
|
|
('diagonal', (M, M, M), (2, 0, 1), '3d_2'),
|
|
('diagonal', (M, M, M), (-2, 0, 1), '3d_3'),
|
|
('tril', (M, M), NO_ARGS),
|
|
('tril', (M, M), (2,), 'idx'),
|
|
('tril', (S, M, M), NO_ARGS, 'batched'),
|
|
('tril', (S, M, M), (2,), 'batched_idx'),
|
|
('tril', (3, 3, S, S), NO_ARGS, 'more_batched'),
|
|
('triu', (M, M), NO_ARGS),
|
|
('triu', (M, M), (2,), 'idx'),
|
|
('triu', (S, M, M), NO_ARGS, 'batched'),
|
|
('triu', (S, M, M), (2,), 'batched_idx'),
|
|
('triu', (3, 3, S, S), NO_ARGS, 'more_batched'),
|
|
('trace', (M, M), NO_ARGS),
|
|
('cross', (S, 3), ((S, 3),)),
|
|
('cross', (S, 3, S), ((S, 3, S), 1), 'dim'),
|
|
('index_select', (S, S, S), (0, index_variable(2, S)), 'dim', (), [0]),
|
|
('index_select', (), (0, torch.tensor([0], dtype=torch.int64)), 'scalar_mixed_dim', (), [0]),
|
|
('index_select', (), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_dim', (), [0]),
|
|
('index_add', (S, S), (0, index_variable(2, S), (2, S)), 'dim', (), [0]),
|
|
('index_add', (), (0, torch.tensor([0], dtype=torch.int64), (1,)), 'scalar_input_dim', (), [0]),
|
|
('index_add', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim', (), [0]),
|
|
('index_copy', (S, S), (0, index_perm_variable(2, S), (2, S)), 'dim', (), [0]),
|
|
('index_copy', (), (0, torch.tensor([0], dtype=torch.int64), (1,)), 'scalar_input_dim', (), [0]),
|
|
('index_copy', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim', (), [0]),
|
|
('index_fill', (S, S), (0, index_variable(2, S), 2), 'dim', (), [0]),
|
|
('index_fill', (S, S), (0, index_variable(2, S), ()), 'variable_dim', (), [0]),
|
|
('index_fill', (S, S), (0, torch.tensor(0, dtype=torch.int64), 2), 'scalar_index_dim', (), [0]),
|
|
('index_fill', (), (0, torch.tensor([0], dtype=torch.int64), 2), 'scalar_input_dim', (), [0]),
|
|
('index_fill', (), (0, torch.tensor(0, dtype=torch.int64), 2), 'scalar_both_dim', (), [0]),
|
|
('inverse', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', (), NO_ARGS, [skipIfNoLapack]),
|
|
('inverse', lambda: random_fullrank_matrix_distinct_singular_value(S, 2, 3),
|
|
NO_ARGS, 'batched', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', (S, S), NO_ARGS, '', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', (1, 1), NO_ARGS, '1x1', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_symmetric_matrix(S), NO_ARGS, 'symmetric', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_symmetric_psd_matrix(S), NO_ARGS, 'symmetric_psd', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_square_matrix_of_rank(S, S - 2), NO_ARGS, 'dim2_null', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_square_matrix_of_rank(S, 1), NO_ARGS, 'rank1', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_square_matrix_of_rank(S, 2), NO_ARGS, 'rank2', (), NO_ARGS, [skipIfNoLapack]),
|
|
('det', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS,
|
|
'distinct_singular_values', (), NO_ARGS, [skipIfNoLapack]),
|
|
# For `logdet` and `slogdet`, the function at det=0 is not smooth.
|
|
# We need to exclude tests with det=0 (e.g. dim2_null, rank1, rank2) and use
|
|
# `make_nonzero_det` to make the random matrices have nonzero det. For
|
|
# `logdet`, we also set `make_nonzero_det(matrix, sign=1)` to make the
|
|
# matrix have positive det.
|
|
('logdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, '', (), NO_ARGS, [skipIfNoLapack]),
|
|
('logdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1', (), NO_ARGS, [skipIfNoLapack]),
|
|
('logdet', lambda: make_nonzero_det(random_symmetric_matrix(S), 1), NO_ARGS,
|
|
'symmetric', (), NO_ARGS, [skipIfNoLapack]),
|
|
('logdet', lambda: make_nonzero_det(random_symmetric_pd_matrix(S), 1), NO_ARGS,
|
|
'symmetric_pd', (), NO_ARGS, [skipIfNoLapack]),
|
|
('logdet', lambda: make_nonzero_det(random_fullrank_matrix_distinct_singular_value(S), 1, 0), NO_ARGS,
|
|
'distinct_singular_values', (), NO_ARGS, [skipIfNoLapack]),
|
|
('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS,
|
|
'1x1_pos_det', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), -1), NO_ARGS,
|
|
'1x1_neg_det', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS,
|
|
'pos_det', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: make_nonzero_det(torch.randn(S, S), -1), NO_ARGS,
|
|
'neg_det', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: make_nonzero_det(random_symmetric_matrix(S)), NO_ARGS,
|
|
'symmetric', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: random_symmetric_pd_matrix(S), NO_ARGS,
|
|
'symmetric_pd', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('slogdet', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS,
|
|
'distinct_singular_values', (), NO_ARGS, [skipIfNoLapack], itemgetter(1)),
|
|
('symeig', lambda: random_symmetric_matrix(S), (True, False), 'lower', (), NO_ARGS, [skipIfNoLapack]),
|
|
('symeig', lambda: random_symmetric_matrix(S), (True, True), 'upper', (), NO_ARGS, [skipIfNoLapack]),
|
|
('symeig', lambda: random_symmetric_matrix(M), (True, True), 'large', (), NO_ARGS, [skipIfNoLapack]),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', (), NO_ARGS, [skipIfNoLapack]),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], NO_ARGS,
|
|
'wide', (), NO_ARGS, [skipIfNoLapack]),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], NO_ARGS,
|
|
'tall', (), NO_ARGS, [skipIfNoLapack]),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], (False,),
|
|
'wide_all', (), NO_ARGS, [skipIfNoLapack], lambda usv: (usv[0], usv[1], usv[2][:, :(S - 2)])),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], (False,),
|
|
'tall_all', (), NO_ARGS, [skipIfNoLapack], lambda usv: (usv[0][:, :(S - 2)], usv[1], usv[2])),
|
|
('svd', lambda: random_fullrank_matrix_distinct_singular_value(M), NO_ARGS,
|
|
'large', (), NO_ARGS, [skipIfNoLapack]),
|
|
('solve', (S, S), (random_fullrank_matrix_distinct_singular_value(
|
|
S, silent=True),), '', (), NO_ARGS, [skipIfNoLapack]),
|
|
('solve', (S, S, S), (random_fullrank_matrix_distinct_singular_value(S, S, silent=True),),
|
|
'batched', (), NO_ARGS, [skipIfNoLapack]),
|
|
('solve', (2, 3, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 3, silent=True),),
|
|
'batched_dims', (), NO_ARGS, [skipIfNoLapack]),
|
|
('solve', (2, 2, S, S), (random_fullrank_matrix_distinct_singular_value(S, 1, silent=True),),
|
|
'batched_broadcast_A', (), NO_ARGS, [skipIfNoLapack]),
|
|
('solve', (1, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 2, silent=True),),
|
|
'batched_broadcast_b', (), NO_ARGS, [skipIfNoLapack]),
|
|
('fill_', (S, S, S), (1,), 'number'),
|
|
('fill_', (), (1,), 'number_scalar'),
|
|
('fill_', (S, S, S), ((),), 'variable'),
|
|
('eq_', (S, S, S), ((S, S, S),)),
|
|
('eq_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('eq_', (), ((),), 'scalar'),
|
|
('eq_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('ne_', (S, S, S), ((S, S, S),)),
|
|
('ne_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('ne_', (), ((),), 'scalar'),
|
|
('ne_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('gt_', (S, S, S), ((S, S, S),)),
|
|
('gt_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('gt_', (), ((),), 'scalar'),
|
|
('gt_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('ge_', (S, S, S), ((S, S, S),)),
|
|
('ge_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('ge_', (), ((),), 'scalar'),
|
|
('ge_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('lt_', (S, S, S), ((S, S, S),)),
|
|
('lt_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('lt_', (), ((),), 'scalar'),
|
|
('lt_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('le_', (S, S, S), ((S, S, S),)),
|
|
('le_', (S, S, S), ((1,),), 'broadcast_rhs'),
|
|
('le_', (), ((),), 'scalar'),
|
|
('le_', (S, S, S), ((),), 'scalar_broadcast_rhs'),
|
|
('eq_', (S, S, S), (0,), 'pyscalar'),
|
|
('ne_', (S, S, S), (0,), 'pyscalar'),
|
|
('gt_', (S, S, S), (0,), 'pyscalar'),
|
|
('ge_', (S, S, S), (0,), 'pyscalar'),
|
|
('le_', (S, S, S), (0,), 'pyscalar'),
|
|
('lt_', (), (0,), 'pyscalar'),
|
|
('eq_', (), (0,), 'pyscalar_scalar'),
|
|
('ne_', (), (0,), 'pyscalar_scalar'),
|
|
('gt_', (), (0,), 'pyscalar_scalar'),
|
|
('ge_', (), (0,), 'pyscalar_scalar'),
|
|
('lt_', (), (0,), 'pyscalar_scalar'),
|
|
('le_', (), (0,), 'pyscalar_scalar'),
|
|
('permute', (1, 2, 3, 4), (0, 2, 3, 1), '', (True,)),
|
|
('permute', (1, 2, 3, 4), (0, -2, -1, 1), 'neg_dim', (True,)),
|
|
('permute', (), (dont_convert(()),), 'scalar', (True,)),
|
|
('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, '', (True,)),
|
|
('squeeze', (1, 1, 1, 1), NO_ARGS, 'input_sizes_are_ones', (True,)),
|
|
('squeeze', (S, 1, S, 1), (1,), '1_dim', (True,), [0]),
|
|
('squeeze', (S, 1, S, 1), (2,), 'not_1_dim', (True,), [0]),
|
|
('squeeze', (), (0,), 'scalar', (True,), [0]),
|
|
('unsqueeze', (S, S, S), (0,), 'first', (True,), [0]),
|
|
('unsqueeze', (S, S, S), (1,), 'middle', (True,), [0]),
|
|
('unsqueeze', (S, S, S), (3,), 'last', (True,), [0]),
|
|
('unsqueeze', (), (0,), 'scalar', (True,), [0]),
|
|
('chunk', (S, S, S), (2,), '', (True, 'prim::ConstantChunk')),
|
|
('chunk', (S, S, S), (S, 1), 'dim', (True, 'prim::ConstantChunk'), [1]),
|
|
('split', (S, S, S), (2,)),
|
|
('split', (S, S, S), (S, 1), 'dim', (), [1]),
|
|
('split', (S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],), 'size_list'),
|
|
('split', (S, S, S), ([int(S / 2), S - int(S / 2) * 2, int(S / 2)], 2), 'size_list_dim', (), [1]),
|
|
('gather', (M, S), (0, gather_variable((S, S), 1, M, True)), 'dim0', (), [0]),
|
|
('gather', (M, S), (1, gather_variable((M, S // 2), 0, S, True)), 'dim1', (), [0]),
|
|
('gather', (), (0, torch.tensor([0], dtype=torch.int64)), 'scalar_input', (), [0]),
|
|
('gather', (S,), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_index', (), [0]),
|
|
('gather', (), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_both', (), [0]),
|
|
('scatter', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', (), [0]),
|
|
('scatter', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', (), [0]),
|
|
('scatter', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalartensor_all_dim0', (), [0]),
|
|
('scatter', (), (0, torch.tensor(0, dtype=torch.int64), 2.5), 'scalar_all_dim0', (), [0]),
|
|
('scatter_add', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', (), [0]),
|
|
('scatter_add', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', (), [0]),
|
|
('scatter_add', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim0', (), [0]),
|
|
('masked_select', (M, M), (mask_not_all_zeros((M, M)),)),
|
|
('masked_select', (M, M), (mask_not_all_zeros((M,)),), 'broadcast_rhs'),
|
|
('masked_select', (M,), (mask_not_all_zeros((M, M)),), 'broadcast_lhs'),
|
|
('masked_select', (M, 1, M), (mask_not_all_zeros((M, M)),),
|
|
'broadcast_all'),
|
|
('masked_select', (), (torch.tensor(1, dtype=torch.uint8),), 'scalar'),
|
|
('masked_select', (M, M), (torch.tensor(1, dtype=torch.uint8),), 'scalar_broadcast_rhs'),
|
|
('masked_select', (), (mask_not_all_zeros((M, M)),), 'scalar_broadcast_lhs'),
|
|
('masked_fill', (M, M), (torch.ByteTensor(M, M).bernoulli_(), 10)),
|
|
('masked_fill', (M, M), (torch.ByteTensor(M, M).bernoulli_(), ()), 'tensor'),
|
|
('masked_fill', (M,), (torch.ByteTensor(M, M).bernoulli_(), 10), 'broadcast_lhs'),
|
|
('masked_fill', (M, M), (torch.ByteTensor(M,).bernoulli_(), 10), 'broadcast_rhs'),
|
|
('masked_fill', (), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), 10), 'scalar'),
|
|
('masked_fill', (), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), ()),
|
|
'scalar_variable'),
|
|
('masked_fill', (M, M), (torch.tensor(0, dtype=torch.uint8).bernoulli_(), 10),
|
|
'scalar_broadcast_rhs'),
|
|
('masked_scatter', (M, M), (torch.ByteTensor(M, M).bernoulli_(), (M, M))),
|
|
('masked_scatter', (M,), (torch.ByteTensor(M, M).bernoulli_(), (M, M)),
|
|
'broadcast_lhs'),
|
|
('masked_scatter', (M, M), (torch.ByteTensor(M,).bernoulli_(), (M, M)),
|
|
'broadcast_rhs'),
|
|
('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)), 'scalar'),
|
|
('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)),
|
|
'scalar_broadcast_rhs'),
|
|
('resize_', (S, S, S), (torch.Size([S * S, S])), 'fewer_dims'),
|
|
('resize_', (), (dont_convert(()),), 'scalar'),
|
|
('resize_', (), (torch.Size([1, 1, 1])), 'scalar_to_dims'),
|
|
('resize_as_', (), (non_differentiable(torch.tensor(5.)),), 'scalar'),
|
|
('resize_as_', (), (non_differentiable(torch.randn((1, 1, 1))),), 'scalar_to_dims'),
|
|
('resize_as_', (S, S, S), (non_differentiable(torch.randn(S * S, S)),)),
|
|
('sort', (S, M, S), NO_ARGS),
|
|
('sort', (S, M, S), (1,), 'dim'),
|
|
('sort', (S, M, S), (1, True), 'dim_desc'),
|
|
('sort', (), NO_ARGS, 'scalar'),
|
|
('sort', (), (0,), 'dim_scalar'),
|
|
('sort', (), (0, True), 'dim_desc_scalar'),
|
|
('topk', (S, M, S), (3,)),
|
|
('topk', (S, M, S), (3, 1), 'dim', (), [1]),
|
|
('topk', (S, M, S), (3, 1, True), 'dim_desc', (), [1]),
|
|
('topk', (S, M, S), (3, 1, True, True), 'dim_desc_sort', (), [1]),
|
|
('topk', (), (1,), 'scalar'),
|
|
('topk', (), (1, 0), 'dim_scalar', (), [1]),
|
|
('topk', (), (1, 0, True), 'dim_desc_scalar', (), [1]),
|
|
('topk', (), (1, 0, True, True), 'dim_desc_sort_scalar', (), [1]),
|
|
('take', (S, S, S), (torch.LongTensor([[-3, 2], [20, 2]]),)),
|
|
('take', (S, S, S), (torch.tensor(0, dtype=torch.int64),), 'scalar_index'),
|
|
('take', (), (torch.LongTensor([0]),), 'scalar_data'),
|
|
('take', (), (torch.tensor(0, dtype=torch.int64),), 'scalar_both'),
|
|
('where', (M, M), (mask_not_all_zeros((M, M)), (M, M)), '', (True,)),
|
|
('where', (M, 1, M), (mask_not_all_zeros((M, M)), (M, M, 1)), 'broadcast_all', (True,)),
|
|
('where', (), (bernoulli_scalar(), ()), 'scalar', (True,)),
|
|
('where', (M, 1, M), (bernoulli_scalar(), (M, M, 1)), 'scalar_broadcast_mask', (True,)),
|
|
('where', (), (mask_not_all_zeros((M, M)), ()), 'scalar_broadcast_non_mask', (True,)),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([1, 2]),)),
|
|
('__getitem__', torch.randn(S, S, S), (slice(0, 3),), 'slice'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([slice(0, 3), 1]),), 'slice_index'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3], [0, 0, 2]]),), 'adv_index'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 0, 3], [1, 1, 3], [0, 0, 2]]),), 'adv_index_dup'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), slice(None), [0, 3]]),), 'adv_index_end'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), [0, 3], slice(None)]),), 'adv_index_mid'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None), slice(None)]),), 'adv_index_beg'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], [1, 2], slice(None)]),), 'adv_index_comb'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], ]),), 'adv_index_sub'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None)]),), 'adv_index_sub_2'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], Ellipsis]),), 'adv_index_sub_3'),
|
|
('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3],
|
|
torch.LongTensor([0, 0, 2])]),), 'adv_index_var'),
|
|
]
|
|
# TODO: clamp with min/max
|
|
|
|
|
|
def create_input(call_args, requires_grad=True, non_contiguous=False, call_kwargs=None):
|
|
if not isinstance(call_args, tuple):
|
|
call_args = (call_args,)
|
|
|
|
def map_arg(arg):
|
|
def maybe_non_contig(tensor):
|
|
return tensor if not non_contiguous else make_non_contiguous(tensor)
|
|
|
|
if isinstance(arg, torch.Size) or isinstance(arg, dont_convert):
|
|
return arg
|
|
elif isinstance(arg, tuple) and len(arg) == 0:
|
|
var = torch.randn((), dtype=torch.double)
|
|
var.requires_grad = requires_grad
|
|
return var
|
|
elif isinstance(arg, tuple) and not isinstance(arg[0], torch.Tensor):
|
|
return Variable(maybe_non_contig(torch.randn(*arg, dtype=torch.double)), requires_grad=requires_grad)
|
|
elif isinstance(arg, non_differentiable):
|
|
if isinstance(arg.tensor, torch.Tensor):
|
|
return maybe_non_contig(arg.tensor)
|
|
return maybe_non_contig(arg.tensor)
|
|
elif isinstance(arg, torch.Tensor):
|
|
if arg.dtype == torch.float:
|
|
arg = arg.double()
|
|
# NOTE: We do clone() after detach() here because we need to be able to change size/storage of v afterwards
|
|
v = maybe_non_contig(arg).detach().clone()
|
|
v.requires_grad = requires_grad and v.is_floating_point()
|
|
return v
|
|
elif callable(arg):
|
|
return map_arg(arg())
|
|
else:
|
|
return arg
|
|
args_out = tuple(map_arg(arg) for arg in call_args)
|
|
kwargs_out = {k: map_arg(v) for k, v in call_kwargs.items()} if call_kwargs else {}
|
|
return args_out, kwargs_out
|
|
|
|
|
|
def _compare_trilu_indices(
|
|
self, row, col, offset=0, dtype=torch.long, device='cpu'):
|
|
if row == 0 or col == 0:
|
|
# have to handle this separately as tril and triu does not take
|
|
# empty matrix as input
|
|
self.assertEqual(
|
|
torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1),
|
|
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
|
|
|
|
self.assertEqual(
|
|
torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1),
|
|
torch.triu_indices(row, col, offset, dtype=dtype, device=device))
|
|
|
|
else:
|
|
self.assertEqual(
|
|
torch.ones(row, col, dtype=dtype, device='cpu')
|
|
.tril(offset).nonzero().transpose(0, 1).to(device),
|
|
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
|
|
|
|
self.assertEqual(
|
|
torch.ones(row, col, dtype=dtype, device='cpu')
|
|
.tril(offset).nonzero().transpose(0, 1).to(device),
|
|
torch.tril_indices(row, col, offset, dtype=dtype, device=device))
|
|
|
|
|
|
def _compare_large_trilu_indices(
|
|
self, row, col, offset=0, dtype=torch.long, device='cpu'):
|
|
l = torch.ones(row, col, dtype=dtype, device='cpu').tril(offset) \
|
|
.nonzero()[-100:-1, :].transpose(0, 1).to(device)
|
|
torch.cuda.empty_cache()
|
|
|
|
r = torch.tril_indices(
|
|
row, col, offset, dtype=dtype, device=device)[:, -100:-1]
|
|
self.assertEqual(l, r)
|
|
torch.cuda.empty_cache()
|
|
|
|
l = torch.ones(row, col, dtype=dtype, device='cpu').triu(offset) \
|
|
.nonzero()[-100:-1, :].transpose(0, 1).to(device)
|
|
torch.cuda.empty_cache()
|
|
|
|
r = torch.triu_indices(
|
|
row, col, offset, dtype=dtype, device=device)[:, -100:-1]
|
|
self.assertEqual(l, r)
|
|
torch.cuda.empty_cache()
|
|
|
|
# (
|
|
# row
|
|
# col
|
|
# offset (optional)
|
|
# dtype (optional)
|
|
# )
|
|
tri_tests_args = [
|
|
(1, 1),
|
|
(3, 3),
|
|
(3, 3, 1),
|
|
(3, 3, 2),
|
|
(3, 3, 200),
|
|
(3, 3, -1),
|
|
(3, 3, -2),
|
|
(3, 3, -200),
|
|
(0, 3, 0),
|
|
(0, 3, 1),
|
|
(0, 3, -1),
|
|
(3, 0, 0),
|
|
(3, 0, 1),
|
|
(3, 0, -1),
|
|
(0, 0, 0),
|
|
(0, 0, 1),
|
|
(0, 0, -1),
|
|
(3, 6, 0),
|
|
(3, 6, 1),
|
|
(3, 6, 3),
|
|
(3, 6, 9),
|
|
(3, 6, -1),
|
|
(3, 6, -3),
|
|
(3, 6, -9),
|
|
(6, 3, 0),
|
|
(6, 3, 1),
|
|
(6, 3, 3),
|
|
(6, 3, 9),
|
|
(6, 3, -1),
|
|
(6, 3, -3),
|
|
(6, 3, -9),
|
|
(258, 253, 1, torch.float32),
|
|
(257, 258, 1, torch.float64),
|
|
(258, 258, 1, torch.short),
|
|
(3, 513, 1, torch.long),
|
|
(513, 3, 1, torch.int),
|
|
(513, 0, 1, torch.double),
|
|
(1024, 1024),
|
|
(1024, 1024, 500, torch.float32),
|
|
(1024, 1024, 1023),
|
|
(1024, 1024, -500),
|
|
(1023, 1025),
|
|
(1025, 1023, 1022),
|
|
(1024, 1024, -500),
|
|
(3, 2028),
|
|
(3, 2028, 1),
|
|
(3, 2028, -1),
|
|
(2028, 3),
|
|
(2028, 1),
|
|
(2028, 1, -1)
|
|
]
|
|
|
|
tri_large_tests_args = [
|
|
# Large test cases below are deliberately commented out to speed up CI
|
|
# tests and to avoid OOM error. When modifying implementations of
|
|
# tril_indices and triu_indices, please enable these tests and make sure
|
|
# they pass.
|
|
#
|
|
# (1, 268435455),
|
|
# (5000, 5000),
|
|
# (10000, 10000),
|
|
# (268435455, 1),
|
|
# (134217727, 2, 1),
|
|
# (2, 134217727, 1),
|
|
# (536870901, 1),
|
|
# (1, 536870901),
|
|
# (268435455, 2, 1),
|
|
# (2, 268435455, 1)
|
|
]
|
|
|
|
|
|
def run_additional_tri_tests(self, device):
|
|
x = torch.ones(
|
|
3, 3, dtype=torch.long, device=device, layout=torch.strided)
|
|
l = x.tril(0).nonzero().transpose(0, 1)
|
|
u = x.triu(0).nonzero().transpose(0, 1)
|
|
self.assertEqual(l, torch.tril_indices(3, 3, device=device))
|
|
self.assertEqual(
|
|
l, torch.tril_indices(3, 3, device=device, layout=torch.strided))
|
|
|
|
self.assertEqual(u, torch.triu_indices(3, 3, device=device))
|
|
self.assertEqual(
|
|
u, torch.triu_indices(3, 3, device=device, layout=torch.strided))
|
|
|
|
self.assertRaises(
|
|
RuntimeError,
|
|
lambda: torch.triu_indices(
|
|
1, 1, device=device, layout=torch.sparse_coo))
|
|
|
|
self.assertRaises(
|
|
RuntimeError,
|
|
lambda: torch.tril_indices(
|
|
1, 1, device=device, layout=torch.sparse_coo))
|
|
|
|
|
|
def unpack_variables(args):
|
|
if istuple(args):
|
|
return tuple(unpack_variables(elem) for elem in args)
|
|
else:
|
|
return args
|
|
|
|
|
|
EXCLUDE_FUNCTIONAL = {
|
|
'addmm',
|
|
'addmm_',
|
|
'addbmm',
|
|
'baddbmm',
|
|
'addmv',
|
|
'addmv_',
|
|
'addr',
|
|
'addr_',
|
|
'reshape',
|
|
'where' # argument order
|
|
}
|
|
EXCLUDE_GRADCHECK = {
|
|
}
|
|
EXCLUDE_GRADGRADCHECK = {
|
|
}
|
|
EXCLUDE_GRADGRADCHECK_BY_TEST_NAME = {
|
|
# *det methods uses svd in backward when matrix is not invertible. However,
|
|
# svd backward is unstable unless the matrix has positive distinct singular
|
|
# values. Generated random matrices satisfy this with high probability, but
|
|
# we can't rely on it. So only test gradgrad on invertible test cases and
|
|
# _distinct_singular_values.
|
|
'test_det',
|
|
'test_det_1x1',
|
|
'test_det_symmetric',
|
|
'test_det_symmetric_psd',
|
|
'test_det_dim2_null',
|
|
'test_det_rank1',
|
|
'test_det_rank2',
|
|
# `other` expand_as(self, other) is not used in autograd.
|
|
'test_expand_as',
|
|
'test_logdet',
|
|
'test_logdet_1x1',
|
|
'test_logdet_symmetric',
|
|
'test_slogdet_1x1_neg_det',
|
|
'test_slogdet_neg_det',
|
|
'test_slogdet_symmetric',
|
|
'test_cdist',
|
|
}
|
|
|
|
|
|
def exclude_tensor_method(name, test_name):
|
|
# there are no tensor equivalents for these (inplace or out)
|
|
exclude_all_tensor_method_by_test_name = {
|
|
'test_clamp_min',
|
|
'test_clamp_max',
|
|
'test_clamp_min_scalar',
|
|
'test_clamp_max_scalar',
|
|
'test_slice',
|
|
'test_where',
|
|
'test_where_broadcast_all',
|
|
'test_where_scalar',
|
|
'test_where_scalar_broadcast_mask',
|
|
'test_where_scalar_broadcast_non_mask',
|
|
}
|
|
# there are no out-of-place tensor equivalents for these
|
|
exclude_outplace_tensor_method = {
|
|
'index_add',
|
|
'index_copy',
|
|
'index_fill',
|
|
'masked_fill',
|
|
'masked_scatter',
|
|
'scatter',
|
|
'scatter_add',
|
|
'det',
|
|
}
|
|
if test_name in exclude_all_tensor_method_by_test_name:
|
|
return True
|
|
is_magic_method = name[:2] == '__' and name[-2:] == '__'
|
|
is_inplace = name[-1] == "_" and not is_magic_method
|
|
if not is_inplace and name in exclude_outplace_tensor_method:
|
|
return True
|
|
return False
|