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test_shape_ops.py
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test_shape_ops.py
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import torch
import numpy as np
from itertools import product, combinations, permutations, chain
from functools import partial
import random
import warnings
from torch._six import nan
from torch.testing._internal.common_utils import (
TestCase, run_tests, make_tensor, torch_to_numpy_dtype_dict)
from torch.testing._internal.common_device_type import (
instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyOnCPUAndCUDA,
dtypesIfCPU, dtypesIfCUDA, largeTensorTest)
# TODO: replace with make_tensor
def _generate_input(shape, dtype, device, with_extremal):
if shape == ():
x = torch.tensor((), dtype=dtype, device=device)
else:
if dtype.is_floating_point or dtype.is_complex:
# work around torch.randn not being implemented for bfloat16
if dtype == torch.bfloat16:
x = torch.randn(*shape, device=device) * random.randint(30, 100)
x = x.to(torch.bfloat16)
else:
x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100)
x[torch.randn(*shape) > 0.5] = 0
if with_extremal and dtype.is_floating_point:
# Use extremal values
x[torch.randn(*shape) > 0.5] = float('nan')
x[torch.randn(*shape) > 0.5] = float('inf')
x[torch.randn(*shape) > 0.5] = float('-inf')
elif with_extremal and dtype.is_complex:
x[torch.randn(*shape) > 0.5] = complex('nan')
x[torch.randn(*shape) > 0.5] = complex('inf')
x[torch.randn(*shape) > 0.5] = complex('-inf')
elif dtype == torch.bool:
x = torch.zeros(shape, dtype=dtype, device=device)
x[torch.randn(*shape) > 0.5] = True
else:
x = torch.randint(15, 100, shape, dtype=dtype, device=device)
return x
class TestShapeOps(TestCase):
# TODO: update to work on CUDA, too
@onlyCPU
def test_unbind(self, device):
x = torch.rand(2, 3, 4, 5)
for dim in range(4):
res = torch.unbind(x, dim)
res2 = x.unbind(dim)
self.assertEqual(x.size(dim), len(res))
self.assertEqual(x.size(dim), len(res2))
for i in range(dim):
self.assertEqual(x.select(dim, i), res[i])
self.assertEqual(x.select(dim, i), res2[i])
# TODO: update to work on CUDA, too?
@onlyCPU
def test_tolist(self, device):
list0D = []
tensor0D = torch.tensor(list0D)
self.assertEqual(tensor0D.tolist(), list0D)
table1D = [1., 2., 3.]
tensor1D = torch.tensor(table1D)
storage = torch.Storage(table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
table2D = [[1, 2], [3, 4]]
tensor2D = torch.tensor(table2D)
self.assertEqual(tensor2D.tolist(), table2D)
tensor3D = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
tensorNonContig = tensor3D.select(1, 1)
self.assertFalse(tensorNonContig.is_contiguous())
self.assertEqual(tensorNonContig.tolist(), [[3, 4], [7, 8]])
@dtypes(torch.int64, torch.float, torch.complex128)
def test_movedim_invalid(self, device, dtype):
shape = self._rand_shape(4, min_size=5, max_size=10)
x = _generate_input(shape, dtype, device, False)
for fn in [torch.movedim, torch.moveaxis]:
# Invalid `source` and `destination` dimension
with self.assertRaisesRegex(IndexError, "Dimension out of range"):
fn(x, 5, 0)
with self.assertRaisesRegex(IndexError, "Dimension out of range"):
fn(x, 0, 5)
# Mismatch in size of `source` and `destination`
with self.assertRaisesRegex(RuntimeError, "movedim: Invalid source or destination dims:"):
fn(x, (1, 0), (0, ))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
fn(x, (0, 0), (0, 1))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
fn(x, (0, 1, 0), (0, 1, 2))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
fn(x, (0, 1), (1, 1))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
fn(x, (0, 1, 2), (1, 0, 1))
@dtypes(torch.int64, torch.float, torch.complex128)
def test_movedim(self, device, dtype):
for fn in [torch.moveaxis, torch.movedim]:
for nd in range(5):
shape = self._rand_shape(nd, min_size=5, max_size=10)
x = _generate_input(shape, dtype, device, with_extremal=False)
for random_negative in [True, False]:
for src_dim, dst_dim in permutations(range(nd), r=2):
random_prob = random.random()
if random_negative and random_prob > 0.66:
src_dim = src_dim - nd
elif random_negative and random_prob > 0.33:
dst_dim = dst_dim - nd
elif random_negative:
src_dim = src_dim - nd
dst_dim = dst_dim - nd
# Integer `source` and `destination`
torch_fn = partial(fn, source=src_dim, destination=dst_dim)
np_fn = partial(np.moveaxis, source=src_dim, destination=dst_dim)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
if nd == 0:
continue
def make_index_negative(sequence, idx):
sequence = list(sequence)
sequence[random_idx] = sequence[random_idx] - nd
return tuple(src_sequence)
for src_sequence in permutations(range(nd), r=random.randint(1, nd)):
# Sequence `source` and `destination`
dst_sequence = tuple(random.sample(range(nd), len(src_sequence)))
# Randomly change a dim to a negative dim representation of itself.
random_prob = random.random()
if random_negative and random_prob > 0.66:
random_idx = random.randint(0, len(src_sequence) - 1)
src_sequence = make_index_negative(src_sequence, random_idx)
elif random_negative and random_prob > 0.33:
random_idx = random.randint(0, len(src_sequence) - 1)
dst_sequence = make_index_negative(dst_sequence, random_idx)
elif random_negative:
random_idx = random.randint(0, len(src_sequence) - 1)
dst_sequence = make_index_negative(dst_sequence, random_idx)
random_idx = random.randint(0, len(src_sequence) - 1)
src_sequence = make_index_negative(src_sequence, random_idx)
torch_fn = partial(fn, source=src_sequence, destination=dst_sequence)
np_fn = partial(np.moveaxis, source=src_sequence, destination=dst_sequence)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
# Move dim to same position
x = torch.randn(2, 3, 5, 7, 11)
torch_fn = partial(fn, source=(0, 1), destination=(0, 1))
np_fn = partial(np.moveaxis, source=(0, 1), destination=(0, 1))
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
torch_fn = partial(fn, source=1, destination=1)
np_fn = partial(np.moveaxis, source=1, destination=1)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
# Empty Sequence
torch_fn = partial(fn, source=(), destination=())
np_fn = partial(np.moveaxis, source=(), destination=())
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
@dtypes(torch.float, torch.bool)
def test_diag(self, device, dtype):
if dtype is torch.bool:
x = torch.rand(100, 100, device=device) >= 0.5
else:
x = torch.rand(100, 100, dtype=dtype, device=device)
res1 = torch.diag(x)
res2 = torch.tensor((), dtype=dtype, device=device)
torch.diag(x, out=res2)
self.assertEqual(res1, res2)
def test_diagonal(self, device):
x = torch.randn((100, 100), device=device)
result = torch.diagonal(x)
expected = torch.diag(x)
self.assertEqual(result, expected)
x = torch.randn((100, 100), device=device)
result = torch.diagonal(x, 17)
expected = torch.diag(x, 17)
self.assertEqual(result, expected)
@onlyCPU
@dtypes(torch.float)
def test_diagonal_multidim(self, device, dtype):
x = torch.randn(10, 11, 12, 13, dtype=dtype, device=device)
xn = x.numpy()
for args in [(2, 2, 3),
(2,),
(-2, 1, 2),
(0, -2, -1)]:
result = torch.diagonal(x, *args)
expected = xn.diagonal(*args)
self.assertEqual(expected.shape, result.shape)
self.assertEqual(expected, result)
# test non-continguous
xp = x.permute(1, 2, 3, 0)
result = torch.diagonal(xp, 0, -2, -1)
expected = xp.numpy().diagonal(0, -2, -1)
self.assertEqual(expected.shape, result.shape)
self.assertEqual(expected, result)
@onlyOnCPUAndCUDA
@dtypesIfCPU(*torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_half=False,
include_bfloat16=False))
@dtypesIfCUDA(*torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_bfloat16=False))
def test_trace(self, device, dtype):
def test(shape):
tensor = make_tensor(shape, device, dtype, low=-9, high=9)
expected_dtype = tensor.sum().dtype
expected_dtype = torch_to_numpy_dtype_dict[expected_dtype]
result = np.trace(tensor.cpu().numpy(), dtype=expected_dtype)
expected = torch.tensor(result, device=device)
self.assertEqual(tensor.trace(), expected)
shapes = (
[10, 1],
[1, 10],
[100, 100],
[20, 100],
[100, 20],
)
for shape in shapes:
test(shape)
def generate_clamp_baseline(self, device, dtype, *, min_vals, max_vals, with_nans):
"""
Creates a random tensor for a given device and dtype, and computes the expected clamped
values given the min_vals and/or max_vals.
If with_nans is provided, then some values are randomly set to nan.
"""
X = torch.rand(100, device=device).mul(50).add(-25) # uniform in [-25, 25]
X = X.to(dtype)
if with_nans:
mask = torch.randint(0, 2, X.shape, dtype=torch.bool, device=device)
X[mask] = nan
if isinstance(min_vals, torch.Tensor):
min_vals = min_vals.cpu().numpy()
if isinstance(max_vals, torch.Tensor):
max_vals = max_vals.cpu().numpy()
# Use NumPy implementation as reference
X_clamped = torch.tensor(np.clip(X.cpu().numpy(), a_min=min_vals, a_max=max_vals), device=device)
return X, X_clamped
# Tests clamp and its alias, clip
@dtypes(torch.int64, torch.float32)
def test_clamp(self, device, dtype):
op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
# min/max argument product
args = product((-10, None), (10, None))
for op in op_list:
for min_val, max_val in args:
if min_val is None and max_val is None:
continue
X, Y_expected = self.generate_clamp_baseline(device, dtype,
min_vals=min_val,
max_vals=max_val,
with_nans=False)
# Test op
X1 = X.clone() # So that the in-place ops do not change X
Y_actual = op(X1, min_val, max_val)
self.assertEqual(Y_expected, Y_actual)
# Test op-out behavior (out does not exist for method versions)
if op in (torch.clamp, torch.clip):
Y_out = torch.empty_like(X)
op(X, min=min_val, max=max_val, out=Y_out)
self.assertEqual(Y_expected, Y_out)
def test_clamp_propagates_nans(self, device):
op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
# min/max argument product
args = product((-10, None), (10, None))
for op in op_list:
for min_val, max_val in args:
if min_val is None and max_val is None:
continue
X, Y_expected = self.generate_clamp_baseline(device, torch.float,
min_vals=min_val,
max_vals=max_val,
with_nans=True)
Y_expected = torch.isnan(Y_expected)
# Test op
X1 = X.clone() # So that the in-place ops do not change X
Y_actual = op(X1, min_val, max_val)
self.assertEqual(Y_expected, torch.isnan(Y_actual))
# Test op-out behavior (out does not exist for method versions)
if op in (torch.clamp, torch.clip):
Y_out = torch.empty_like(X)
op(X, min_val, max_val, out=Y_out)
self.assertEqual(Y_expected, torch.isnan(Y_out))
def test_clamp_raises_arg_errors(self, device):
X = torch.randn(100, dtype=torch.float, device=device)
error_msg = 'At least one of \'min\' or \'max\' must not be None'
with self.assertRaisesRegex(RuntimeError, error_msg):
X.clamp()
with self.assertRaisesRegex(RuntimeError, error_msg):
X.clamp_()
with self.assertRaisesRegex(RuntimeError, error_msg):
torch.clamp(X)
@dtypes(*torch.testing.get_all_dtypes())
def test_flip(self, device, dtype):
make_from_data = partial(torch.tensor, device=device, dtype=dtype)
make_from_size = partial(make_tensor, device=device, dtype=dtype)
def test_flip_impl(input_t, dims, output_t):
def all_t():
yield input_t, output_t
if dtype is torch.float:
# We generate quantized versions as well
for qdtype in (torch.quint8, torch.qint8, torch.qint32):
qinput_t = torch.quantize_per_tensor(input_t, 0.1, 5, qdtype)
qoutput_t = torch.quantize_per_tensor(output_t, 0.1, 5, qdtype)
yield qinput_t, qoutput_t
for in_t, out_t in all_t():
self.assertEqual(in_t.flip(dims), out_t)
n = in_t.ndim
if not isinstance(dims, tuple):
# Wrap dim
self.assertEqual(in_t.flip(-n + dims), out_t)
else:
# Permute dimensions
for p_dims in permutations(dims):
self.assertEqual(in_t.flip(p_dims), out_t)
if len(p_dims) > 0:
# Wrap 1st dim
self.assertEqual(in_t.flip((-n + p_dims[0],) + p_dims[1:]), out_t)
def gen_data():
# Basic tests
data = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2)
nonctg = make_from_size((2, 2, 2), noncontiguous=True).copy_(data)
dims_result = ((0, make_from_data([5, 6, 7, 8, 1, 2, 3, 4]).view(2, 2, 2)),
(1, make_from_data([3, 4, 1, 2, 7, 8, 5, 6]).view(2, 2, 2)),
(2, make_from_data([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2)),
((0, 1), make_from_data([7, 8, 5, 6, 3, 4, 1, 2]).view(2, 2, 2)),
((0, 1, 2), make_from_data([8, 7, 6, 5, 4, 3, 2, 1]).view(2, 2, 2)))
for in_tensor, (dims, out_tensor) in product((data, nonctg), dims_result):
yield in_tensor, dims, out_tensor
# Expanded
in_t = make_from_data([1, 2, 3]).view(3, 1).expand(3, 2)
dims = 0
out_t = make_from_data([3, 3, 2, 2, 1, 1]).view(3, 2)
yield in_t, dims, out_t
# Noop on expanded dimension
yield in_t, 1, in_t
# Transposed
in_t = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2).transpose(0, 1)
dims = (0, 1, 2)
out_t = make_from_data([8, 7, 4, 3, 6, 5, 2, 1]).view(2, 2, 2)
yield in_t, dims, out_t
# Rectangular case
in_t = make_from_data([1, 2, 3, 4, 5, 6]).view(2, 3)
dims = 0
out_t = make_from_data([[4, 5, 6], [1, 2, 3]])
yield in_t, dims, out_t
dims = 1
out_t = make_from_data([[3, 2, 1], [6, 5, 4]])
yield in_t, dims, out_t
# Noops (edge cases)
# Size 0
in_t = make_from_data(())
yield in_t, 0, in_t
yield in_t, (), in_t
# dims = ()
in_t = make_from_size((3, 2, 1))
yield in_t, (), in_t
# Zero elements, non-zero size
in_t = make_from_size((3, 0, 2))
for i in range(in_t.ndim):
yield in_t, i, in_t
# Size 1
in_t = make_from_size(())
yield in_t, 0, in_t
in_t = make_from_size((1,))
yield in_t, 0, in_t
for in_tensor, dims, out_tensor in gen_data():
test_flip_impl(in_tensor, dims, out_tensor)
# test for shape
size = [2, 3, 4]
data = make_from_size(size)
possible_dims = range(len(size))
test_dims = chain(combinations(possible_dims, 1), combinations(possible_dims, 2))
for dims in test_dims:
self.assertEqual(size, list(data.flip(dims).size()))
@dtypes(*torch.testing.get_all_dtypes())
def test_flip_errors(self, device, dtype):
make_arg = partial(make_tensor, dtype=dtype, device=device)
data = make_arg((2, 2, 2))
# not allow flip on the same dim more than once
self.assertRaises(RuntimeError, lambda: data.flip(0, 1, 1))
# not allow empty list as input
self.assertRaises(TypeError, lambda: data.flip())
# not allow dim > max dim
self.assertRaises(IndexError, lambda: data.flip(0, 1, 2, 3))
self.assertRaises(IndexError, lambda: data.flip(3))
def _rand_shape(self, dim, min_size, max_size):
return tuple(torch.randint(min_size, max_size + 1, (dim,)))
@dtypes(*torch.testing.get_all_dtypes())
def test_flip_numpy(self, device, dtype):
make_arg = partial(make_tensor, dtype=dtype, device=device)
for ndim in [3, 4]:
shape = self._rand_shape(ndim, 5, 10)
data = make_arg(shape)
# Axis to sample for given shape.
for i in range(1, ndim + 1):
# Check all combinations of `i` axis.
for flip_dim in combinations(range(ndim), i):
torch_fn = partial(torch.flip, dims=flip_dim)
np_fn = partial(np.flip, axis=flip_dim)
self.compare_with_numpy(torch_fn, np_fn, data)
@onlyCUDA # CPU is too slow
@largeTensorTest('17GB') # 4 tensors of 4GB (in, out) x (torch, numpy) + 1GB
def test_flip_large_tensor(self, device):
t_in = torch.empty(2**32 + 1, dtype=torch.uint8).random_()
torch_fn = partial(torch.flip, dims=(0,))
np_fn = partial(np.flip, axis=0)
self.compare_with_numpy(torch_fn, np_fn, t_in)
del t_in
def _test_fliplr_flipud(self, torch_fn, np_fn, min_dim, max_dim, device, dtype):
for dim in range(min_dim, max_dim + 1):
shape = self._rand_shape(dim, 5, 10)
# Randomly scale the input
if dtype.is_floating_point or dtype.is_complex:
data = torch.randn(*shape, device=device, dtype=dtype)
else:
data = torch.randint(0, 10, shape, device=device, dtype=dtype)
self.compare_with_numpy(torch_fn, np_fn, data)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_fliplr(self, device, dtype):
self._test_fliplr_flipud(torch.fliplr, np.fliplr, 2, 4, device, dtype)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_fliplr_invalid(self, device, dtype):
x = torch.randn(42).to(dtype)
with self.assertRaisesRegex(RuntimeError, "Input must be >= 2-d."):
torch.fliplr(x)
with self.assertRaisesRegex(RuntimeError, "Input must be >= 2-d."):
torch.fliplr(torch.tensor(42, device=device, dtype=dtype))
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_flipud(self, device, dtype):
self._test_fliplr_flipud(torch.flipud, np.flipud, 1, 4, device, dtype)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_flipud_invalid(self, device, dtype):
with self.assertRaisesRegex(RuntimeError, "Input must be >= 1-d."):
torch.flipud(torch.tensor(42, device=device, dtype=dtype))
def test_rot90(self, device):
data = torch.arange(1, 5, device=device).view(2, 2)
self.assertEqual(torch.tensor([1, 2, 3, 4]).view(2, 2), data.rot90(0, [0, 1]))
self.assertEqual(torch.tensor([2, 4, 1, 3]).view(2, 2), data.rot90(1, [0, 1]))
self.assertEqual(torch.tensor([4, 3, 2, 1]).view(2, 2), data.rot90(2, [0, 1]))
self.assertEqual(torch.tensor([3, 1, 4, 2]).view(2, 2), data.rot90(3, [0, 1]))
# test for default args k=1, dims=[0, 1]
self.assertEqual(data.rot90(), data.rot90(1, [0, 1]))
# test for reversed order of dims
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(1, [1, 0]))
# test for modulo of k
self.assertEqual(data.rot90(5, [0, 1]), data.rot90(1, [0, 1]))
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(-1, [0, 1]))
self.assertEqual(data.rot90(-5, [0, 1]), data.rot90(-1, [0, 1]))
# test for dims out-of-range error
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, -3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 2]))
# test tensor with more than 2D
data = torch.arange(1, 9, device=device).view(2, 2, 2)
self.assertEqual(torch.tensor([2, 4, 1, 3, 6, 8, 5, 7]).view(2, 2, 2), data.rot90(1, [1, 2]))
self.assertEqual(data.rot90(1, [1, -1]), data.rot90(1, [1, 2]))
# test for errors
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [1, 1]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 1, 2]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0]))
@dtypes(torch.cfloat, torch.cdouble)
def test_complex_rot90(self, device, dtype):
shape = self._rand_shape(random.randint(2, 4), 5, 10)
for rot_times in range(4):
data = torch.randn(*shape, device=device, dtype=dtype)
torch_fn = partial(torch.rot90, k=rot_times, dims=[0, 1])
np_fn = partial(np.rot90, k=rot_times, axes=[0, 1])
self.compare_with_numpy(torch_fn, np_fn, data)
# TODO: update once warning flag is available to always trigger ONCE warnings
# Ensures nonzero does not throw a warning, even when the as_tuple argument
# is not provided
def test_nonzero_no_warning(self, device):
t = torch.randn((2, 2), device=device)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
torch.nonzero(t)
t.nonzero()
self.assertEqual(len(w), 0)
@dtypes(*torch.testing.get_all_dtypes(include_complex=False))
def test_nonzero(self, device, dtype):
shapes = [
torch.Size((12,)),
torch.Size((12, 1)),
torch.Size((1, 12)),
torch.Size((6, 2)),
torch.Size((3, 2, 2)),
torch.Size((5, 5, 5)),
]
def gen_nontrivial_input(shape, dtype, device):
if dtype != torch.bfloat16:
return torch.randint(2, shape, device=device, dtype=dtype)
else:
# windows does not work for bfloat16 randing
return torch.randint(2, shape, device=device, dtype=torch.float).to(dtype)
for shape in shapes:
tensor = gen_nontrivial_input(shape, dtype, device)
dst1 = torch.nonzero(tensor, as_tuple=False)
dst2 = tensor.nonzero(as_tuple=False)
dst3 = torch.empty([], dtype=torch.long, device=device)
torch.nonzero(tensor, out=dst3)
if self.device_type != 'xla':
# xla does not raise runtime error
self.assertRaisesRegex(
RuntimeError,
"scalar type Long",
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.float, device=device))
)
if self.device_type == 'cuda':
self.assertRaisesRegex(
RuntimeError,
"on the same device",
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.long))
)
np_array = tensor.cpu().numpy() if dtype != torch.bfloat16 else tensor.float().cpu().numpy()
np_result = torch.from_numpy(np.stack(np_array.nonzero())).t()
self.assertEqual(dst1.cpu(), np_result, atol=0, rtol=0)
self.assertEqual(dst2.cpu(), np_result, atol=0, rtol=0)
self.assertEqual(dst3.cpu(), np_result, atol=0, rtol=0)
tup1 = torch.nonzero(tensor, as_tuple=True)
tup2 = tensor.nonzero(as_tuple=True)
tup1 = torch.stack(tup1).t().cpu()
tup2 = torch.stack(tup2).t().cpu()
self.assertEqual(tup1, np_result, atol=0, rtol=0)
self.assertEqual(tup2, np_result, atol=0, rtol=0)
def test_nonzero_astuple_out(self, device):
t = torch.randn((3, 3, 3), device=device)
out = torch.empty_like(t, dtype=torch.long)
with self.assertRaises(RuntimeError):
torch.nonzero(t, as_tuple=True, out=out)
self.assertEqual(torch.nonzero(t, as_tuple=False, out=out), torch.nonzero(t, out=out))
# Verifies that JIT script cannot handle the as_tuple kwarg
# See Issue https://github.com/pytorch/pytorch/issues/45499.
def _foo(t):
tuple_result = torch.nonzero(t, as_tuple=True)
nontuple_result = torch.nonzero(t, as_tuple=False)
out = torch.empty_like(nontuple_result)
torch.nonzero(t, as_tuple=False, out=out)
return tuple_result, nontuple_result, out
with self.assertRaises(RuntimeError):
scripted_foo = torch.jit.script(_foo)
# Verifies that JIT tracing works fine
traced_foo = torch.jit.trace(_foo, t)
traced_tuple, traced_nontuple, traced_out = traced_foo(t)
expected_tuple = torch.nonzero(t, as_tuple=True)
expected_nontuple = torch.nonzero(t)
self.assertEqual(traced_tuple, expected_tuple)
self.assertEqual(traced_nontuple, expected_nontuple)
self.assertEqual(traced_out, expected_nontuple)
@onlyOnCPUAndCUDA
def test_nonzero_discontiguous(self, device):
shape = (4, 4)
tensor = torch.randint(2, shape, device=device)
tensor_nc = torch.empty(shape[0], shape[1] * 2, device=device)[:, ::2].copy_(tensor)
dst1 = tensor.nonzero(as_tuple=False)
dst2 = tensor_nc.nonzero(as_tuple=False)
self.assertEqual(dst1, dst2, atol=0, rtol=0)
dst3 = torch.empty_like(dst1)
data_ptr = dst3.data_ptr()
# expect dst3 storage to be reused
torch.nonzero(tensor, out=dst3)
self.assertEqual(data_ptr, dst3.data_ptr())
self.assertEqual(dst1, dst3, atol=0, rtol=0)
# discontiguous out
dst4 = torch.empty(dst1.size(0), dst1.size(1) * 2, dtype=torch.long, device=device)[:, ::2]
data_ptr = dst4.data_ptr()
strides = dst4.stride()
torch.nonzero(tensor, out=dst4)
self.assertEqual(data_ptr, dst4.data_ptr())
self.assertEqual(dst1, dst4, atol=0, rtol=0)
self.assertEqual(strides, dst4.stride())
def test_nonzero_non_diff(self, device):
x = torch.randn(10, requires_grad=True)
nz = x.nonzero()
self.assertFalse(nz.requires_grad)
instantiate_device_type_tests(TestShapeOps, globals())
if __name__ == '__main__':
run_tests()