blob: c65ae980fd82af62e23d2086bcf73f7aa7b0526d [file] [log] [blame]
import torch
import numpy as np
import warnings
import math
from itertools import product, chain
from numbers import Number
import random
import unittest
from torch._six import inf, nan
from torch.testing._internal.common_utils import (
TestCase, run_tests, torch_to_numpy_dtype_dict, numpy_to_torch_dtype_dict,
suppress_warnings, TEST_SCIPY, slowTest, skipIfNoSciPy, IS_WINDOWS)
from torch.testing._internal.common_methods_invocations import (
unary_ufuncs, _NOTHING)
from torch.testing._internal.common_device_type import (
instantiate_device_type_tests, ops, dtypes, onlyCPU, onlyOnCPUAndCUDA,
onlyCUDA, dtypesIfCUDA, precisionOverride, skipCUDAIfRocm, dtypesIfCPU,
OpDTypes)
from torch.testing import make_tensor
from torch.testing._internal.common_dtype import (
floating_types_and, all_types_and_complex_and, floating_and_complex_types_and, get_all_dtypes, get_all_math_dtypes,
get_all_int_dtypes, get_all_fp_dtypes, get_all_complex_dtypes
)
if TEST_SCIPY:
import scipy
# Refer [scipy reference filter]
# Filter operators for which the reference function
# is available in the current environment (for reference_numerics tests).
reference_filtered_ops = list(filter(lambda op: op.ref is not _NOTHING, unary_ufuncs))
# Tests for unary "universal functions (ufuncs)" that accept a single
# tensor and have common properties like:
# - they are elementwise functions
# - the input shape is the output shape
# - they typically have method and inplace variants
# - they typically support the out kwarg
# - they typically have NumPy or SciPy references
# See NumPy's universal function documentation
# (https://numpy.org/doc/1.18/reference/ufuncs.html) for more details
# about the concept of ufuncs.
# Functions tested here:
#
# Interesting values and extremal values for different dtypes
_unsigned_int_vals = (0, 1, 55, 127)
_int_vals = (0, -1, 1, -55, 55, -127, 127, -128, 128)
_large_int_vals = (-1113, 1113, -10701, 10701)
_float_vals = (0.,
-.001, .001,
-.25, .25,
-1., 1.,
-math.pi / 2, math.pi / 2,
-math.pi + .00001, math.pi - .00001,
-math.pi, math.pi,
-math.pi - .00001, math.pi + .00001)
_large_float16_vals = (-501, 501,
-1001.2, 1001.2,
-13437.7, 13437.7)
_large_float_vals = _large_float16_vals + (-4988429.2, 4988429.2, -1e20, 1e20)
_float_extremals = (float('inf'), float('-inf'), float('nan'))
_medium_length = 812
_large_size = (1029, 917)
# Returns generator of tensors of different sizes filled with values in domain
# and with intested region filled with `vals`. This will help test different code
# paths for the given vals
def generate_tensors_from_vals(vals, device, dtype, domain):
offset = 63
assert _large_size[1] > (_medium_length + offset) # large tensor should be large enough
assert len(vals) < _medium_length # medium tensor should contain all vals
assert _medium_length % 4 == 0 # ensure vectorized code coverage
if not dtype.is_complex:
# Filter values based on Operators domain.
# Note: Complex numbers don't belong to ordered field,
# so we don't filter for them.
if domain[0] is not None:
vals = list(filter(lambda x: x >= domain[0], vals))
if domain[1] is not None:
vals = list(filter(lambda x: x < domain[1], vals))
# Constructs the large tensor containing vals
large_tensor = make_tensor(_large_size, device=device, dtype=dtype, low=domain[0], high=domain[1])
# Inserts the vals at an odd place
large_tensor[57][offset:offset + len(vals)] = torch.tensor(vals, device=device, dtype=dtype)
# Takes a medium sized copy of the large tensor containing vals
medium_tensor = large_tensor[57][offset:offset + _medium_length]
# Constructs scalar tensors
scalar_tensors = (t.squeeze() for t in torch.split(medium_tensor, 1))
# Tensors with no elements
empty_sizes = ((0,), (0, 3, 3), (1, 0, 5), (6, 0, 0, 0), (3, 0, 1, 0))
empty_tensors = (torch.empty(size, device=device, dtype=dtype) for size in empty_sizes)
return chain(empty_tensors, scalar_tensors, (medium_tensor,), (large_tensor,))
# [Note generate_numeric_tensors, generate_numeric_tensors_hard,
# and generate_numeric_tensors_extremal]
#
# Returns an iterable of contiguous tensors with the same storage on the requested
# device and with the requested dtype.
#
# This function is intended to test the non-vectorized and vectorized code
# paths of unary functions, as well as their handling of odd tensor
# sizes (like zero-dim tensors and tensors with zero elements).
#
# The iterable will include an empty tensor, tensors with no elements,
# zero dim (scalar) tensors, small 1D tensors, a medium 1D tensor, and
# a large 2D tensor.
#
# These tensors will include interesting values. The generate_numeric_tensors_hard
# tests larger values (>500) and generate_numeric_tensors_extremal tests extremal
# values like -inf, inf, and nan.
#
# The randomly generated values can be restricted by the domain
# argument.
def generate_numeric_tensors(device, dtype, *,
domain=(None, None)):
# Special-cases bool
if dtype is torch.bool:
tensors = (torch.empty(0, device=device, dtype=torch.bool),
torch.tensor(True, device=device),
torch.tensor(False, device=device),
torch.tensor((True, False), device=device),
make_tensor((_medium_length,), device=device, dtype=dtype, low=None, high=None),
make_tensor(_large_size, device=device, dtype=dtype, low=None, high=None))
return tensors
# Acquires dtype-specific vals
if dtype.is_floating_point or dtype.is_complex:
vals = _float_vals
# Converts float -> complex vals if dtype is complex
if dtype.is_complex:
vals = tuple(complex(x, y) for x, y in product(vals, vals))
elif dtype is torch.uint8:
vals = _unsigned_int_vals
else: # dtypes is a signed integer type
assert dtype in (torch.int8, torch.int16, torch.int32, torch.int64)
vals = _int_vals
return generate_tensors_from_vals(vals, device, dtype, domain)
def generate_numeric_tensors_hard(device, dtype, *,
domain=(None, None)):
is_signed_integral = dtype in (torch.int8, torch.int16, torch.int32, torch.int64)
if not (dtype.is_floating_point or dtype.is_complex or is_signed_integral):
return ()
if dtype.is_floating_point:
if dtype is torch.float16:
# float16 has smaller range.
vals = _large_float16_vals
else:
vals = _large_float_vals
elif dtype.is_complex:
vals = tuple(complex(x, y) for x, y in chain(product(_large_float_vals, _large_float_vals),
product(_float_vals, _large_float_vals),
product(_large_float_vals, _float_vals)))
else:
vals = _large_int_vals
return generate_tensors_from_vals(vals, device, dtype, domain)
def generate_numeric_tensors_extremal(device, dtype, *,
domain=(None, None)):
if not (dtype.is_floating_point or dtype.is_complex):
return ()
vals = []
if dtype.is_floating_point:
vals = _float_extremals
elif dtype.is_complex:
vals = tuple(complex(x, y) for x, y in chain(product(_float_extremals, _float_extremals),
product(_float_vals, _float_extremals),
product(_float_extremals, _float_vals)))
return generate_tensors_from_vals(vals, device, dtype, domain)
# TODO: port test_unary_out_op_mem_overlap
# TODO: add out= tests (different devices, dtypes, mismatched sizes,
# correct sizes, 0 size, broadcasted out)
# TODO: add test for inplace variants erroring on broadcasted inputs
class TestUnaryUfuncs(TestCase):
exact_dtype = True
# Tests bool tensor negation raises the correct error
def test_neg_error_message(self, device):
msg = ("Negation, the `\\-` operator, on a bool tensor is not supported."
" If you are trying to invert a mask, use the `\\~` or"
" `logical_not\\(\\)` operator instead.")
t = torch.tensor((False, True), device=device)
with self.assertRaisesRegex(RuntimeError, msg):
torch.neg(t)
@dtypes(*floating_types_and(torch.bfloat16, torch.half))
@ops((_fn for _fn in unary_ufuncs if _fn.domain != (None, None)))
def test_float_domains(self, device, dtype, op):
if not op.supports_dtype(dtype, torch.device(device).type):
raise unittest.SkipTest('unsupported dtype')
eps = (1e-5, 1e-3, 1e-1, 1, 2, 10, 20, 50, 100)
low, high = op.domain
# NOTE: the following two loops are separated for readability
if low is not None:
low_tensor = torch.tensor(low, device=device, dtype=dtype)
for epsilon in eps:
lower_tensor = low_tensor - epsilon
# Skips the test if the difference is not representable,
# which can occur if, for example, the difference is small
# and the dtype is imprecise (like bfloat16 is)
if lower_tensor.item() == low_tensor.item():
continue
result = op(lower_tensor)
self.assertEqual(result.item(), float('nan'),
msg=("input of {0} outside lower domain boundary"
" {1} produced {2}, not nan!").format(lower_tensor.item(),
low,
result.item()))
if high is not None:
high_tensor = torch.tensor(high, device=device, dtype=dtype)
for epsilon in eps:
higher_tensor = high_tensor + epsilon
# See above comment
if higher_tensor.item() == high_tensor.item():
continue
result = op(higher_tensor)
self.assertEqual(result.item(), float('nan'),
msg=("input of {0} outside upper domain boundary"
" {1} produced {2}, not nan!").format(higher_tensor.item(),
high,
result.item()))
# Helper for comparing torch tensors and numpy arrays
# TODO: should this or assertEqual also validate that strides are equal?
def assertEqualHelper(self, actual, expected, msg, *, dtype, exact_dtype=True, **kwargs):
assert isinstance(actual, torch.Tensor)
# Some NumPy functions return scalars, not arrays
if isinstance(expected, Number):
self.assertEqual(actual.item(), expected, **kwargs)
elif isinstance(expected, np.ndarray):
# Handles exact dtype comparisons between arrays and tensors
if exact_dtype:
# Allows array dtype to be float32 when comparing with bfloat16 tensors
# since NumPy doesn't support the bfloat16 dtype
# Also ops like scipy.special.erf, scipy.special.erfc, etc, promote float16
# to float32
if expected.dtype == np.float32:
assert actual.dtype in (torch.float16, torch.bfloat16, torch.float32)
else:
assert expected.dtype == torch_to_numpy_dtype_dict[actual.dtype]
self.assertEqual(actual,
torch.from_numpy(expected).to(actual.dtype),
msg,
exact_device=False,
**kwargs)
else:
self.assertEqual(actual, expected, msg, exact_device=False, **kwargs)
# Tests that the function and its (array-accepting) reference produce the same
# values on given tensors
def _test_reference_numerics(self, dtype, op, tensors, equal_nan=True):
def _helper_reference_numerics(expected, actual, msg, exact_dtype, equal_nan=True):
if not torch.can_cast(numpy_to_torch_dtype_dict[expected.dtype.type], dtype):
exact_dtype = False
if dtype in [torch.uint8, torch.int8, torch.bool]:
# NOTE: For these dtypes, PyTorch computes in the default scalar type (float)
# while NumPy computes in float16
self.assertEqualHelper(actual, expected, msg, dtype=dtype,
exact_dtype=exact_dtype, rtol=1e-3, atol=1e-2)
elif dtype is torch.bfloat16:
# Ref: https://github.com/pytorch/pytorch/blob/master/torch/testing/_internal/common_utils.py#L1149
self.assertEqualHelper(actual, expected, msg, dtype=dtype,
exact_dtype=exact_dtype, rtol=16e-3, atol=1e-5)
else:
self.assertEqualHelper(actual, expected, msg, dtype=dtype, equal_nan=equal_nan, exact_dtype=exact_dtype)
for t in tensors:
torch_kwargs, numpy_kwargs = op.sample_kwargs(t.device, dtype, t)
if dtype is torch.bfloat16:
a = t.cpu().to(torch.float32).numpy()
else:
a = t.cpu().numpy()
actual = op(t, **torch_kwargs)
expected = op.ref(a, **numpy_kwargs)
# Crafts a custom error message for smaller, printable tensors
if t.numel() < 10:
msg = ("Failed to produce expected results! Input tensor was"
" {0}, torch result is {1}, and reference result is"
" {2}.").format(t, actual, expected)
else:
msg = None
exact_dtype = True
if isinstance(actual, torch.Tensor):
_helper_reference_numerics(expected, actual, msg, exact_dtype, equal_nan)
else:
for x, y in zip(expected, actual):
# testing multi-outputs results
_helper_reference_numerics(x, y, msg, exact_dtype, equal_nan)
# Tests that the function and its (array-accepting) reference produce the same
# values on a range of tensors, including empty tensors, scalar tensors,
# 1D tensors and a large 2D tensor with interesting and extremal values
# and noncontiguities.
@suppress_warnings
@ops(reference_filtered_ops)
def test_reference_numerics_normal(self, device, dtype, op):
tensors = generate_numeric_tensors(device, dtype,
domain=op.domain)
self._test_reference_numerics(dtype, op, tensors)
@suppress_warnings
@ops(reference_filtered_ops, allowed_dtypes=floating_and_complex_types_and(
torch.bfloat16, torch.half, torch.int8, torch.int16, torch.int32, torch.int64
))
def test_reference_numerics_hard(self, device, dtype, op):
if not op.handles_large_floats:
raise self.skipTest("This op does not handle large values")
tensors = generate_numeric_tensors_hard(device, dtype,
domain=op.domain)
self._test_reference_numerics(dtype, op, tensors)
@suppress_warnings
@ops(reference_filtered_ops,
allowed_dtypes=floating_and_complex_types_and(torch.bfloat16, torch.half))
def test_reference_numerics_extremal(self, device, dtype, op):
handles_extremals = (op.handles_complex_extremals if
dtype in (torch.cfloat, torch.cdouble) else op.handles_extremals)
if not handles_extremals:
raise self.skipTest("This op does not handle extremal values")
tensors = generate_numeric_tensors_extremal(device, dtype,
domain=op.domain)
self._test_reference_numerics(dtype, op, tensors)
# Tests for testing (non)contiguity consistency
@ops(unary_ufuncs)
def test_contig_vs_every_other(self, device, dtype, op):
contig = make_tensor((1026,), device=device, dtype=dtype,
low=op.domain[0], high=op.domain[1])
non_contig = contig[::2]
self.assertTrue(contig.is_contiguous())
self.assertFalse(non_contig.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, non_contig)
self.assertEqual(op(contig, **torch_kwargs)[::2], op(non_contig, **torch_kwargs))
@ops(unary_ufuncs)
def test_contig_vs_transposed(self, device, dtype, op):
contig = make_tensor((789, 357), device=device, dtype=dtype,
low=op.domain[0], high=op.domain[1])
non_contig = contig.T
self.assertTrue(contig.is_contiguous())
self.assertFalse(non_contig.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
self.assertEqual(op(contig, **torch_kwargs).T, op(non_contig, **torch_kwargs))
@ops(unary_ufuncs)
def test_non_contig(self, device, dtype, op):
shapes = [(5, 7), (1024,)]
for shape in shapes:
contig = make_tensor(shape, device, dtype,
low=op.domain[0], high=op.domain[1])
non_contig = torch.empty(shape + (2,), device=device, dtype=dtype)[..., 0]
non_contig.copy_(contig)
self.assertTrue(contig.is_contiguous())
self.assertFalse(non_contig.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs))
@ops(unary_ufuncs)
def test_non_contig_index(self, device, dtype, op):
contig = make_tensor((2, 2, 1, 2), device, dtype,
low=op.domain[0], high=op.domain[1])
non_contig = contig[:, 1, ...]
contig = non_contig.contiguous()
self.assertTrue(contig.is_contiguous())
self.assertFalse(non_contig.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs))
@ops(unary_ufuncs)
def test_non_contig_expand(self, device, dtype, op):
shapes = [(1, 3), (1, 7), (5, 7)]
for shape in shapes:
contig = make_tensor(shape, device, dtype,
low=op.domain[0], high=op.domain[1])
non_contig = contig.clone().expand(3, -1, -1)
self.assertTrue(contig.is_contiguous())
self.assertFalse(non_contig.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
contig = op(contig, **torch_kwargs)
non_contig = op(non_contig, **torch_kwargs)
for i in range(3):
self.assertEqual(contig, non_contig[i],
msg='non-contiguous expand[' + str(i) + ']')
@ops(unary_ufuncs)
def test_contig_size1(self, device, dtype, op):
contig = make_tensor((5, 100), device, dtype,
low=op.domain[0], high=op.domain[1])
contig = contig[:1, :50]
contig2 = torch.empty(contig.size(), device=device, dtype=dtype)
contig2.copy_(contig)
self.assertTrue(contig.is_contiguous())
self.assertTrue(contig2.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
self.assertEqual(op(contig, **torch_kwargs), op(contig2, **torch_kwargs))
@ops(unary_ufuncs)
def test_contig_size1_large_dim(self, device, dtype, op):
contig = make_tensor((5, 2, 3, 1, 4, 5, 3, 2, 1, 2, 3, 4), device, dtype,
low=op.domain[0], high=op.domain[1])
contig = contig[:1, :, :, :, :, :, :, :, :, :, :, :]
contig2 = torch.empty(contig.size(), device=device, dtype=dtype)
contig2.copy_(contig)
self.assertTrue(contig.is_contiguous())
self.assertTrue(contig2.is_contiguous())
torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
self.assertEqual(op(contig, **torch_kwargs), op(contig2, **torch_kwargs))
# Tests that computation on a multiple batches is the same as
# per-batch computation.
@ops(unary_ufuncs)
def test_batch_vs_slicing(self, device, dtype, op):
input = make_tensor((1024, 512), dtype=dtype, device=device,
low=op.domain[0], high=op.domain[1])
torch_kwargs, _ = op.sample_kwargs(device, dtype, input)
actual = op(input, **torch_kwargs)
expected = torch.stack([op(slice, **torch_kwargs) for slice in input])
self.assertEqual(actual, expected)
def _test_out_arg(self, op, input, output, expected, **kwargs):
if op.safe_casts_outputs:
expect_fail = not torch.can_cast(expected.dtype, output.dtype)
else:
expect_fail = output.dtype != expected.dtype
if expect_fail:
with self.assertRaises(RuntimeError):
op(input, out=output, **kwargs)
else:
res = op(input, out=output, **kwargs)
self.assertTrue(res is output)
self.assertEqual(output, expected.to(output.dtype))
@ops(unary_ufuncs, dtypes=OpDTypes.supported)
def test_out_arg_all_dtypes(self, device, dtype, op):
if not op.supports_out:
self.skipTest("Skipped! Op doesn't support out= kwarg.")
input = make_tensor((64, 64), dtype=dtype, device=device,
low=op.domain[0], high=op.domain[1])
torch_kwargs, _ = op.sample_kwargs(device, dtype, input)
expected = op(input, **torch_kwargs)
for out_dtype in all_types_and_complex_and(torch.bool, torch.half):
out = torch.empty_like(input, dtype=out_dtype)
self._test_out_arg(op, input, out, expected, **torch_kwargs)
@dtypes(*(get_all_int_dtypes() + [torch.bool] +
get_all_fp_dtypes(include_bfloat16=False)))
def test_nan_to_num(self, device, dtype):
for contiguous in [False, True]:
x = make_tensor((64, 64), low=0., high=100., dtype=dtype, device=device)
if dtype.is_floating_point:
# Add extremal values.
extremals = [float('nan'), float('inf'), -float('inf')]
for idx, extremal in zip(torch.randint(0, 63, (3,)), extremals):
x[idx, :] = extremal
if not contiguous:
x = x.T
# With args
nan = random.random()
posinf = random.random() * 5
neginf = random.random() * 10
self.compare_with_numpy(lambda x: x.nan_to_num(nan=nan, posinf=posinf),
lambda x: np.nan_to_num(x, nan=nan, posinf=posinf),
x)
self.compare_with_numpy(lambda x: x.nan_to_num(posinf=posinf, neginf=neginf),
lambda x: np.nan_to_num(x, posinf=posinf, neginf=neginf),
x)
# Out Variant
out = torch.empty_like(x)
result = torch.nan_to_num(x)
torch.nan_to_num(x, out=out)
self.assertEqual(result, out)
result = torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf)
torch.nan_to_num(x, out=out, nan=nan, posinf=posinf, neginf=neginf)
self.assertEqual(result, out)
@dtypes(torch.cdouble)
def test_complex_edge_values(self, device, dtype):
# sqrt Test Reference: https://github.com/pytorch/pytorch/pull/47424
x = torch.tensor(0. - 1.0e+20j, dtype=dtype, device=device)
self.compare_with_numpy(torch.sqrt, np.sqrt, x)
# acos test reference: https://github.com/pytorch/pytorch/issue/42952
# Skip on Windows, as CUDA acos returns conjugate value
# see https://github.com/pytorch/pytorch/issues/52299
if not (IS_WINDOWS and dtype == torch.cdouble and "cuda" in device):
self.compare_with_numpy(torch.acos, np.arccos, x)
x = torch.tensor((-1.0e+60 if dtype == torch.cdouble else -1.0e+20) - 4988429.2j, dtype=dtype, device=device)
self.compare_with_numpy(torch.sqrt, np.sqrt, x)
@unittest.skipIf(not TEST_SCIPY, "Requires SciPy")
@dtypes(torch.float, torch.double)
def test_digamma_special(self, device, dtype):
# Based on SciPy test for the following special values.
# Reference:
# https://github.com/scipy/scipy/blob/3a8a3a1d4657254a6611e77e9c28feafa26e6645/scipy/special/tests/test_digamma.py#L22
euler = 0.57721566490153286
dataset = [(0., -0.),
(1, -euler),
(0.5, -2 * math.log(2) - euler),
(1 / 3, -math.pi / (2 * math.sqrt(3)) - 3 * math.log(3) / 2 - euler),
(1 / 4, -math.pi / 2 - 3 * math.log(2) - euler),
(1 / 6, -math.pi * math.sqrt(3) / 2 - 2 * math.log(2) - 3 * math.log(3) / 2 - euler),
(1 / 8, -math.pi / 2 - 4 * math.log(2) -
(math.pi + math.log(2 + math.sqrt(2)) - math.log(2 - math.sqrt(2))) / math.sqrt(2) - euler)]
x = torch.tensor(dataset, device=device, dtype=dtype)
self.compare_with_numpy(torch.digamma, scipy.special.digamma, x)
@unittest.skipIf(not TEST_SCIPY, "Requires SciPy")
@dtypes(torch.float, torch.double)
def test_digamma(self, device, dtype):
# Tests pole behavior
tensor = torch.tensor([-0.999999994, -1.999999994, -2.0000000111,
-100.99999994, 0.000000111, -1931.99999994,
-0.000000111, 0, -0, -1, -2, -931], dtype=dtype, device=device)
self.compare_with_numpy(torch.digamma, scipy.special.digamma, tensor)
@skipCUDAIfRocm
@dtypes(*get_all_fp_dtypes(include_half=True, include_bfloat16=False))
def test_frexp(self, device, dtype):
input = make_tensor((50, 50), device, dtype)
mantissa, exponent = torch.frexp(input)
np_mantissa, np_exponent = np.frexp(input.cpu().numpy())
self.assertEqual(mantissa, np_mantissa)
self.assertEqual(exponent, np_exponent)
# torch.frexp returns exponent in int32 to be compatible with np.frexp
self.assertTrue(exponent.dtype == torch.int32)
self.assertTrue(torch_to_numpy_dtype_dict[exponent.dtype] == np_exponent.dtype)
@skipCUDAIfRocm
@dtypes(*get_all_fp_dtypes(include_half=True, include_bfloat16=False))
def test_frexp_out(self, device, dtype):
input = make_tensor((50, 50), device, dtype)
outputs = (
(torch.empty_like(input), torch.empty_like(input, dtype=torch.int)),
(torch.empty_like(input).transpose(0, 1), make_tensor((50, 50), device, torch.int, noncontiguous=True)),
)
for mantissa, exponent in outputs:
torch.frexp(input, out=(mantissa, exponent))
np_mantissa, np_exponent = np.frexp(input.cpu().numpy())
self.assertEqual(mantissa, np_mantissa)
self.assertEqual(exponent, np_exponent)
# The warning is given when output tensors have wrong shape
with warnings.catch_warnings(record=True) as w:
mantissa = torch.empty((2, 2), device=device, dtype=dtype)
exponent = torch.empty((5, 5), device=device, dtype=torch.int)
torch.frexp(input, out=(mantissa, exponent))
self.assertEqual(len(w), 2)
self.assertTrue("An output with one or more elements was resized" in str(w[0].message))
self.assertTrue("An output with one or more elements was resized" in str(w[1].message))
@skipCUDAIfRocm
def test_frexp_assert_raises(self, device):
invalid_input_dtypes = get_all_int_dtypes() + \
get_all_complex_dtypes() + \
[torch.bool]
for dtype in invalid_input_dtypes:
input = make_tensor((50, 50), device, dtype)
with self.assertRaisesRegex(RuntimeError, r"torch\.frexp\(\) only supports floating-point dtypes"):
torch.frexp(input)
for dtype in get_all_fp_dtypes(include_half=True, include_bfloat16=False):
input = make_tensor((50, 50), device, dtype)
dtypes = list(all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
dtypes.remove(dtype)
for mantissa_dtype in dtypes:
mantissa = torch.empty_like(input, dtype=mantissa_dtype)
exponent = torch.empty_like(input, dtype=torch.int)
with self.assertRaisesRegex(RuntimeError,
r"torch\.frexp\(\) expects mantissa to have dtype .+ but got .+"):
torch.frexp(input, out=(mantissa, exponent))
dtypes.append(dtype)
dtypes.remove(torch.int)
for exponent_dtype in dtypes:
mantissa = torch.empty_like(input)
exponent = torch.empty_like(input, dtype=exponent_dtype)
with self.assertRaisesRegex(RuntimeError,
r"torch\.frexp\(\) expects exponent to have int dtype but got .+"):
torch.frexp(input, out=(mantissa, exponent))
def test_mvlgamma_argcheck(self, device):
def run_test(d):
input = torch.linspace((d - 2) / 2, 10, 10, device=device)
torch.mvlgamma(input, d)
with self.assertRaisesRegex(RuntimeError, r"All elements must be greater than \(p-1\)/2"):
run_test(3)
def test_polygamma_neg(self, device):
with self.assertRaisesRegex(RuntimeError, r'polygamma\(n, x\) does not support negative n\.'):
torch.polygamma(-1, torch.tensor([1.0, 2.0], device=device))
# TODO resolve with opinfos
@onlyCPU
def test_op_invert(self, device):
res = 0xffff - torch.arange(127, dtype=torch.int8)
for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
a = torch.arange(127, dtype=dtype)
self.assertEqual(res.to(dtype), ~a)
self.assertEqual(torch.tensor([True, False]), ~torch.tensor([False, True]))
# test exceptions
for dtype in (torch.half, torch.float, torch.double):
a = torch.zeros(10, dtype=dtype)
with self.assertRaises(TypeError):
b = ~a
@dtypes(torch.complex64, torch.complex128)
def test_abs_angle_complex_to_float(self, device, dtype):
# Constructs random complex values
from random import random
random_vals = []
for multiplier in (-1, 1, -10, 10, -100, 100):
for _ in range(10):
random_vals.append(complex(random() * multiplier, random() * multiplier))
for vals in (random_vals, []):
a = np.array(vals, dtype=torch_to_numpy_dtype_dict[dtype])
t = torch.tensor(vals, device=device, dtype=dtype)
for fn_name in ('abs', 'angle'):
torch_fn = getattr(torch, fn_name)
np_fn = getattr(np, fn_name)
# Tests function
np_result = torch.from_numpy(np_fn(a))
torch_result = torch_fn(t).cpu()
self.assertEqual(np_result, torch_result, exact_dtype=True)
# Tests float out
float_dtype = torch.float32 if dtype is torch.complex64 else torch.float64
np_float_out = np_fn(a).astype(torch_to_numpy_dtype_dict[float_dtype])
float_out = torch.empty_like(t).float()
torch_fn(t, out=float_out)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(torch.from_numpy(np_float_out), float_out.cpu())
# Tests float out (resized out)
float_out = torch.empty(1, device=device, dtype=float_dtype)
torch_fn(t, out=float_out)
self.assertEqual(torch.from_numpy(np_float_out), float_out.cpu())
# Tests complex out
np_complex_out = np_fn(a)
complex_out = torch.empty_like(t)
torch_fn(t, out=complex_out)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(torch.from_numpy(np_complex_out), complex_out.cpu())
# Tests complex out (resized out)
complex_out = torch.empty(0, device=device, dtype=dtype)
torch_fn(t, out=complex_out)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(torch.from_numpy(np_complex_out), complex_out.cpu())
# Tests long out behavior (expected failure)
long_out = torch.empty(0, device=device, dtype=torch.long)
with self.assertRaises(RuntimeError):
torch_fn(t, out=long_out)
# Tests inplace
if fn_name == 'abs':
torch_inplace_method = getattr(torch.Tensor, fn_name + "_")
np_fn(a, out=a)
if dtype.is_complex:
with self.assertRaisesRegex(RuntimeError, "In-place abs is not supported for complex tensors."):
torch_inplace_method(t)
return
torch_inplace_method(t)
self.assertEqual(torch.from_numpy(a), t.cpu())
# Note: angle does not have an in-place variant
if fn_name == 'angle':
with self.assertRaises(AttributeError):
torch_inplace_method = getattr(torch.Tensor, fn_name + "_")
def check_internal_mem_overlap(self, inplace_op, num_inputs,
dtype, device,
expected_failure=False):
if isinstance(inplace_op, str):
inplace_op = getattr(torch.Tensor, inplace_op)
input = torch.randn(1, dtype=dtype, device=device).expand(3, 3)
inputs = [input] + [torch.randn_like(input)
for i in range(num_inputs - 1)]
if not expected_failure:
with self.assertRaisesRegex(RuntimeError, 'single memory location'):
inplace_op(*inputs)
else:
with self.assertRaises(AssertionError):
with self.assertRaisesRegex(RuntimeError, 'single memory location'):
inplace_op(*inputs)
def unary_check_input_output_mem_overlap(self, data, sz, op,
expected_failure=False):
def _test(op, output, input):
output_exp = torch.empty_like(output)
op(input, out=output_exp)
self.assertEqual(op(input, out=output), output_exp, msg=op.__name__)
# output is identical to input:
_test(op, output=data[0:sz], input=data[0:sz])
# output and input are independent:
_test(op, output=data[0:sz], input=data[sz:2 * sz])
# output partially overlaps with input:
if not expected_failure:
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, data[0:sz], data[1:sz + 1])
else:
with self.assertRaises(AssertionError):
with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
_test(op, data[0:sz], data[1:sz + 1])
# TODO: run on non-native device types
@dtypes(torch.double)
def test_unary_out_op_mem_overlap(self, device, dtype):
sz = 3
doubles = torch.randn(2 * sz, dtype=dtype, device=device)
positives = torch.randint(1, 100, (2 * sz,), device=device).double()
ints = torch.randint(-100, 100, (2 * sz,), device=device)
unary_mem_overlap_cases = [
("abs", doubles, True, True, 'cpu'),
("abs", doubles, True, True, 'cuda'),
("acos", doubles, True, True, 'cpu'),
("acos", doubles, True, True, 'cuda'),
("asin", doubles, True, True, 'cpu'),
("asin", doubles, True, True, 'cuda'),
("atan", doubles, True, True, 'cpu'),
("atan", doubles, True, True, 'cuda'),
("acosh", doubles, True, True, 'cpu'),
("acosh", doubles, True, True, 'cuda'),
("asinh", doubles, True, True, 'cpu'),
("asinh", doubles, True, True, 'cuda'),
("atanh", doubles, True, True, 'cpu'),
("atanh", doubles, True, True, 'cuda'),
("bitwise_not", ints, True, True, 'cpu'),
("bitwise_not", ints, True, True, 'cuda'),
("ceil", doubles, True, True, 'cpu'),
("ceil", doubles, True, True, 'cuda'),
("cos", doubles, True, True, 'cpu'),
("cos", doubles, True, True, 'cuda'),
("cosh", doubles, True, True, 'cpu'),
("cosh", doubles, True, True, 'cuda'),
("digamma", doubles, True, True, 'cpu'),
("erf", doubles, True, True, 'cpu'),
("erf", doubles, True, True, 'cuda'),
("erfc", doubles, True, True, 'cpu'),
("erfc", doubles, True, True, 'cuda'),
("erfinv", doubles, True, True, 'cpu'),
("erfinv", doubles, True, True, 'cuda'),
("exp", doubles, True, True, 'cpu'),
("exp", doubles, True, True, 'cuda'),
("exp2", doubles, True, True, 'cpu'),
("exp2", doubles, True, True, 'cuda'),
("expm1", doubles, True, True, 'cpu'),
("expm1", doubles, True, True, 'cuda'),
("floor", doubles, True, True, 'cpu'),
("floor", doubles, True, True, 'cuda'),
("frac", doubles, True, True, 'cpu'),
("frac", doubles, True, True, 'cuda'),
("i0", doubles, True, True, 'cpu'),
("i0", doubles, True, True, 'cuda'),
("log", positives, True, True, 'cpu'),
("log", positives, True, True, 'cuda'),
("log10", positives, True, True, 'cpu'),
("log10", positives, True, True, 'cuda'),
("log1p", positives, True, True, 'cpu'),
("log1p", positives, True, True, 'cuda'),
("log2", positives, True, True, 'cpu'),
("log2", positives, True, True, 'cuda'),
("neg", doubles, True, True, 'cpu'),
("neg", doubles, True, True, 'cuda'),
("reciprocal", doubles, True, True, 'cpu'),
("reciprocal", doubles, True, True, 'cuda'),
("round", doubles, True, True, 'cpu'),
("round", doubles, True, True, 'cuda'),
("rsqrt", positives, True, True, 'cpu'),
("rsqrt", positives, True, True, 'cuda'),
("sin", doubles, True, True, 'cpu'),
("sin", doubles, True, True, 'cuda'),
("sinh", doubles, True, True, 'cpu'),
("sinh", doubles, False, True, 'cuda'),
("sigmoid", doubles, True, True, 'cpu'),
("sigmoid", doubles, True, True, 'cuda'),
("logit", doubles, True, True, 'cpu'),
("logit", doubles, True, True, 'cuda'),
("sqrt", doubles, True, True, 'cpu'),
("sqrt", doubles, False, True, 'cuda'),
("tan", doubles, True, True, 'cpu'),
("tan", doubles, True, True, 'cuda'),
("tanh", doubles, True, True, 'cpu'),
("tanh", doubles, True, True, 'cuda'),
("trunc", doubles, True, True, 'cpu'),
("trunc", doubles, True, True, 'cuda')
]
for (fn, inputs, has_input_output_mem_overlap_check,
has_internal_mem_overlap_check, dev) in unary_mem_overlap_cases:
if dev != device:
continue
out_fn = getattr(torch, fn)
in_fn = getattr(torch.Tensor, fn + '_')
self.unary_check_input_output_mem_overlap(inputs, sz, out_fn,
expected_failure=not has_input_output_mem_overlap_check)
self.check_internal_mem_overlap(in_fn, 1, dtype, dev,
expected_failure=not has_internal_mem_overlap_check)
# TODO: opinfo hardshrink
@onlyCPU
@dtypes(torch.float, torch.double)
def test_hardshrink(self, device, dtype):
data = torch.tensor([1, 0.5, 0.3, 0.6], dtype=dtype, device=device).view(2, 2)
self.assertEqual(torch.tensor([1, 0.5, 0, 0.6], dtype=dtype, device=device).view(2, 2),
data.hardshrink(0.3))
self.assertEqual(torch.tensor([1, 0, 0, 0.6], dtype=dtype, device=device).view(2, 2),
data.hardshrink(0.5))
# test default lambd=0.5
self.assertEqual(data.hardshrink(), data.hardshrink(0.5))
# test non-contiguous case
self.assertEqual(torch.tensor([1, 0, 0.5, 0.6], dtype=dtype, device=device).view(2, 2),
data.t().hardshrink(0.3))
@onlyCPU
@dtypes(torch.float, torch.double)
def test_hardshrink_edge_cases(self, device, dtype) -> None:
def h(values, l_expected):
for l, expected in l_expected.items():
values_tensor = torch.tensor([float(v) for v in values],
dtype=dtype, device=device)
expected_tensor = torch.tensor([float(v) for v in expected],
dtype=dtype, device=device)
self.assertEqual(expected_tensor == values_tensor.hardshrink(l),
torch.ones_like(values_tensor, dtype=torch.bool))
def test_helper(min, max):
h([0.0, min, -min, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf],
{0.0: [0.0, min, -min, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf],
min: [0.0, 0.0, 0.0, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf],
0.1: [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, max, -max, inf, -inf],
1.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, max, -max, inf, -inf],
max: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, inf, -inf],
inf: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]})
test_helper(torch.finfo(dtype).tiny, torch.finfo(dtype).max)
@onlyCPU
@slowTest
@dtypes(torch.float)
def test_exp_slow(self, device, dtype):
# Test for https://github.com/pytorch/pytorch/issues/17271
# This is pretty slow on my Macbook but it only takes a few
# seconds on a beefy Xeon server
a = torch.exp(torch.ones(2 ** 31, dtype=dtype, device=device))
b = torch.exp(torch.ones(1, dtype=dtype, device=device))
self.assertEqual(a, b.expand(2 ** 31))
@precisionOverride({torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002})
@dtypesIfCUDA(torch.float, torch.double, torch.bfloat16)
@dtypes(torch.float, torch.double)
def test_hardswish(self, device, dtype):
inputValues = [-1000, -4, -3, -2, 0, 2, 3, 4, 1000]
expectedOutput = np.multiply(
inputValues,
np.minimum(np.maximum((np.add(inputValues, 3)), 0), 6) / 6.0)
inputTensor = torch.tensor(inputValues, dtype=dtype, device=device)
expectedOutputTensor = \
torch.tensor(expectedOutput, dtype=dtype, device=device)
# normal
self.assertEqual(torch.nn.functional.hardswish(inputTensor),
expectedOutputTensor)
# inplace
inputTensorCpy = inputTensor.clone().detach()
torch.nn.functional.hardswish(inputTensorCpy, inplace=True)
self.assertEqual(inputTensorCpy, expectedOutputTensor)
@precisionOverride({torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002})
@dtypesIfCUDA(torch.float, torch.double, torch.bfloat16)
@dtypes(torch.float, torch.double)
def test_hardsigmoid(self, device, dtype):
inputValues = [-1000, -4, -3, -2, 0, 2, 3, 4, 1000]
expectedOutput = np.minimum(np.maximum((np.add(inputValues, 3)), 0), 6) / 6.0
inputTensor = torch.tensor(inputValues, dtype=dtype, device=device)
# normal
self.assertEqual(torch.nn.functional.hardsigmoid(inputTensor),
torch.tensor(expectedOutput, dtype=dtype, device=device))
# inplace
inputTensorCpy = inputTensor.clone().detach()
self.assertEqual(torch.nn.functional.hardsigmoid(inputTensorCpy, inplace=True),
torch.tensor(expectedOutput, dtype=dtype, device=device))
@precisionOverride({torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002})
@dtypesIfCUDA(torch.float, torch.double, torch.bfloat16)
@dtypes(torch.float, torch.double)
def test_hardsigmoid_backward(self, device, dtype):
inputValues = [-3.0, 3.0, -2.0, 2.0, -6.0, 6.0]
expectedValues = [0.0, 0.0, 1.0 / 6.0, 1.0 / 6.0, 0.0, 0.0]
inputTensor = torch.tensor(inputValues, dtype=dtype, device=device).requires_grad_()
expetedTensor = torch.tensor(expectedValues, dtype=dtype, device=device)
out = torch.nn.functional.hardsigmoid(inputTensor)
out.backward(torch.ones_like(inputTensor))
self.assertEqual(inputTensor.grad, expetedTensor)
@skipIfNoSciPy
@dtypes(torch.float, torch.double)
def test_silu(self, device, dtype):
input_np = np.random.randn(5, 8)
special_input = [[-1000, -1, -0.1, 0, 0.5, 1, 2, 1000]]
input_np = np.concatenate((input_np, special_input), axis=0).astype(
torch_to_numpy_dtype_dict[dtype])
expected_output_np = input_np * scipy.special.expit(input_np)
expected_output = torch.from_numpy(expected_output_np).to(device)
expected_output_noncontig = expected_output.transpose(0, 1)
atol = 1e-6
rtol = 1e-6
input = torch.from_numpy(input_np).clone().contiguous().to(device)
self.assertEqual(torch.nn.functional.silu(input), expected_output,
atol=atol, rtol=rtol)
self.assertEqual(torch.nn.functional.silu(input, inplace=True),
expected_output, atol=atol, rtol=rtol)
input = torch.from_numpy(input_np).clone().to(device)
input_noncontig = input.transpose(0, 1)
self.assertEqual(torch.nn.functional.silu(input_noncontig),
expected_output_noncontig, atol=atol, rtol=rtol)
self.assertEqual(torch.nn.functional.silu(
input_noncontig, inplace=True), expected_output_noncontig,
atol=atol, rtol=rtol)
@skipIfNoSciPy
@dtypes(torch.float, torch.double)
def test_mish(self, device, dtype):
input_np = np.random.randn(5, 8)
special_input = [[-1000, -1, -0.1, 0, 0.5, 1, 2, 1000]]
input_np = np.concatenate((input_np, special_input), axis=0).astype(
torch_to_numpy_dtype_dict[dtype])
expected_output_np = input_np * np.tanh(np.log1p(np.exp(input_np)))
expected_output = torch.from_numpy(expected_output_np).to(device)
expected_output_noncontig = expected_output.transpose(0, 1)
atol = 1e-6
rtol = 1e-6
input = torch.from_numpy(input_np).clone().contiguous().to(device)
self.assertEqual(torch.nn.functional.mish(input), expected_output,
atol=atol, rtol=rtol)
self.assertEqual(torch.nn.functional.mish(input, inplace=True),
expected_output, atol=atol, rtol=rtol)
input = torch.from_numpy(input_np).clone().to(device)
input_noncontig = input.transpose(0, 1)
self.assertEqual(torch.nn.functional.mish(input_noncontig),
expected_output_noncontig, atol=atol, rtol=rtol)
self.assertEqual(torch.nn.functional.mish(
input_noncontig, inplace=True), expected_output_noncontig,
atol=atol, rtol=rtol)
# do ops like threshold need a test_unary(_nonufunc) test suite?
@onlyCPU
@dtypes(*get_all_math_dtypes('cpu'))
def test_threshold(self, device, dtype):
if dtype != torch.uint8 and dtype != torch.float16 and not dtype.is_complex:
# 100 is wide enough to use AVX2 instructions for all types
x = torch.randn(100, dtype=torch.float, device=device).sign().to(dtype=dtype)
y = torch.threshold(x, 0, 0)
self.assertTrue(y.le(0).any())
def _helper_test_igamma(self, loglo, loghi, device, dtype,
torch_fcn, scipy_fcn):
exp1 = 2.71828182846
vec1 = torch.logspace(loglo, loghi, steps=500, base=exp1,
dtype=torch.float64, device=device).unsqueeze(-1)
vec1 = vec1.to(dtype)
inputs = [
(vec1, vec1.transpose(0, 1)),
(vec1, vec1), # for large number, it should approach 0.5
(vec1, 0.5 * vec1), # test for considerable ratio
(vec1, 2.0 * vec1),
(vec1[::2, :], vec1[::2, :]), # contiguous/noncontiguous tests
(vec1[::2, :], vec1[:vec1.shape[0] // 2, :]),
(vec1[:vec1.shape[0] // 2, :], vec1[::2, :]),
]
half_prec = dtype in [torch.bfloat16, torch.float16]
for input0, input1 in inputs:
actual = torch_fcn(input0, input1)
if half_prec:
input0 = input0.to(torch.float)
input1 = input1.to(torch.float)
expected = scipy_fcn(input0.cpu().numpy(), input1.cpu().numpy())
expected = torch.from_numpy(expected).to(dtype)
self.assertEqual(actual, expected)
@skipCUDAIfRocm # see issue https://github.com/pytorch/pytorch/issues/46531
@dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64)
@dtypes(torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
@onlyOnCPUAndCUDA
def test_igamma_common(self, device, dtype):
# test igamma for reasonable range of values
loglo = -4 # approx 0.018
loghi = 4 # approx 54.6
self._helper_test_igamma(loglo, loghi, device, dtype,
torch.igamma, scipy.special.gammainc)
@dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64)
@dtypes(torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
@onlyOnCPUAndCUDA
def test_igammac_common(self, device, dtype):
# test igammac for reasonable range of values
loglo = -4 # approx 0.018
loghi = 4 # approx 54.6
self._helper_test_igamma(loglo, loghi, device, dtype,
torch.igammac, scipy.special.gammaincc)
@dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64)
@dtypes(torch.float32, torch.float64)
@onlyOnCPUAndCUDA
def test_igamma_edge_cases(self, device, dtype):
tkwargs = {"dtype": dtype, "device": device}
infs = torch.zeros((3,), **tkwargs) + float("inf")
zeros = torch.zeros((3,), **tkwargs)
ones = torch.ones((3,), **tkwargs)
zero_to_large = torch.tensor([0., 1., 1e3], **tkwargs)
small_to_inf = torch.tensor([1e-3, 1., float("inf")], **tkwargs)
nans = torch.zeros((3,), **tkwargs) + float("nan")
inpouts = [
# (a , x), out
((zeros, small_to_inf), ones),
((small_to_inf, zeros), zeros),
((infs, zero_to_large), zeros),
((zero_to_large, infs), ones),
((zeros, zeros), nans),
((infs, infs), nans),
((-small_to_inf, small_to_inf), nans),
]
for inputs, output in inpouts:
input0, input1 = inputs
calc = torch.igamma(input0, input1)
if torch.all(torch.isnan(output)):
self.assertTrue(torch.all(torch.isnan(calc)))
else:
self.assertEqual(calc, output)
@dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64)
@dtypes(torch.float32, torch.float64)
@onlyOnCPUAndCUDA
def test_igammac_edge_cases(self, device, dtype):
tkwargs = {"dtype": dtype, "device": device}
infs = torch.zeros((3,), **tkwargs) + float("inf")
zeros = torch.zeros((3,), **tkwargs)
ones = torch.ones((3,), **tkwargs)
zero_to_large = torch.tensor([0., 1., 1e3], **tkwargs)
small_to_inf = torch.tensor([1e-3, 1., float("inf")], **tkwargs)
nans = torch.zeros((3,), **tkwargs) + float("nan")
inpouts = [
# (a , x), out
((zeros, small_to_inf), zeros),
((small_to_inf, zeros), ones),
((infs, zero_to_large), ones),
((zero_to_large, infs), zeros),
((zeros, zeros), nans),
((infs, infs), nans),
((-small_to_inf, small_to_inf), nans),
]
for inputs, output in inpouts:
input0, input1 = inputs
calc = torch.igammac(input0, input1)
if torch.all(torch.isnan(output)):
self.assertTrue(torch.all(torch.isnan(calc)))
else:
self.assertEqual(calc, output)
def _i0_helper(self, t):
# Test by comparing to scipy
dtype = t.dtype
actual = torch.i0(t)
if dtype is torch.bfloat16:
t = t.to(torch.float32)
expected = scipy.special.i0(t.cpu().numpy())
# Casting down for dtype float16 is required since scipy upcasts to float32
if dtype is torch.bfloat16 or dtype is torch.float16:
expected = torch.from_numpy(expected).to(dtype)
self.assertEqual(actual, expected)
def _i0_range_helper(self, range, device, dtype):
# i0 tests are broken up by the domain for which the function does not overflow for each dtype
# This is done to ensure that the function performs well across all possible input values, without worrying
# about inf or nan possibilities
for r in (range, -range):
t = torch.rand(1000, device=device).to(dtype) * r
self._i0_helper(t)
@dtypesIfCUDA(*get_all_fp_dtypes())
@dtypes(torch.bfloat16, torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_i0_range1(self, device, dtype):
# This tests the domain for i0 for which float16 does not overflow
# The domain is (-13.25, 13.25)
self._i0_range_helper(13.25, device, dtype)
@dtypesIfCUDA(*get_all_fp_dtypes())
@dtypes(torch.bfloat16, torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_i0_range2(self, device, dtype):
# This tests the domain for i0 for which float32 and bfloat16 does not overflow
# The domain is (-88.5, 88.5)
self._i0_range_helper(88.5, device, dtype)
@dtypes(torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_i0_range3(self, device, dtype):
# This tests the domain for i0 for which float64 does not overflow
# The domain is (-709.75, 709.75)
self._i0_range_helper(709.75, device, dtype)
@dtypesIfCUDA(*get_all_fp_dtypes())
@dtypes(torch.bfloat16, torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_i0_special(self, device, dtype):
t = torch.tensor([], device=device, dtype=dtype)
self._i0_helper(t)
t = torch.tensor([inf, -inf, nan], device=device, dtype=dtype)
self.assertTrue(torch.i0(t).isnan().all())
@dtypesIfCUDA(*get_all_fp_dtypes())
@dtypes(torch.bfloat16, torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_special_i0_i1_vs_scipy(self, device, dtype):
def check_equal(t, torch_fn, scipy_fn):
# Test by comparing to scipy
actual = torch_fn(t)
if dtype is torch.bfloat16:
t = t.to(torch.float32)
expected = scipy_fn(t.cpu().numpy())
# Casting down for dtype float16 is required since scipy upcasts to float32
if dtype is torch.bfloat16 or dtype is torch.float16:
expected = torch.from_numpy(expected).to(dtype)
self.assertEqual(actual, expected)
t = torch.tensor([], device=device, dtype=dtype)
check_equal(t, torch.i0, scipy.special.i0)
check_equal(t, torch.special.i0e, scipy.special.i0e)
if dtype not in [torch.half, torch.bfloat16]:
check_equal(t, torch.special.i1, scipy.special.i1)
check_equal(t, torch.special.i1e, scipy.special.i1e)
range = (-1e7, 1e7)
if dtype == torch.half:
range = (-65000, 65000)
t = torch.linspace(*range, int(1e4), device=device, dtype=dtype)
check_equal(t, torch.i0, scipy.special.i0)
check_equal(t, torch.special.i0e, scipy.special.i0e)
if dtype not in [torch.half, torch.bfloat16]:
check_equal(t, torch.special.i1, scipy.special.i1)
check_equal(t, torch.special.i1e, scipy.special.i1e)
# NaN, inf, -inf are tested in reference_numerics tests.
info = torch.finfo(dtype)
min, max, eps, tiny = info.min, info.max, info.eps, info.tiny
t = torch.tensor([min, max, eps, tiny], dtype=dtype, device=device)
check_equal(t, torch.i0, scipy.special.i0)
check_equal(t, torch.special.i0e, scipy.special.i0e)
if dtype not in [torch.half, torch.bfloat16]:
check_equal(t, torch.special.i1, scipy.special.i1)
check_equal(t, torch.special.i1e, scipy.special.i1e)
@dtypes(torch.float32, torch.float64)
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
def test_special_ndtr_vs_scipy(self, device, dtype):
def check_equal(t):
# Test by comparing to scipy
actual = torch.special.ndtr(t)
expected = scipy.special.ndtr(t.cpu().numpy())
self.assertEqual(actual, expected)
range = (-10, 10)
t = torch.linspace(*range, int(1e4), device=device, dtype=dtype)
check_equal(t)
# NaN, inf, -inf are tested in reference_numerics tests.
info = torch.finfo(dtype)
min, max, eps, tiny = info.min, info.max, info.eps, info.tiny
t = torch.tensor([min, max, eps, tiny], dtype=dtype, device=device)
check_equal(t)
# TODO: allow large opinfo values to be opted-into via metadata
@dtypes(torch.long)
def test_abs_big_number(self, device, dtype):
bignumber = 2 ** 31 + 1
res = torch.tensor([bignumber], device=device, dtype=dtype)
self.assertGreater(res.abs()[0], 0)
# TODO: add signed zero testing to opinfos
@dtypes(torch.float, torch.double)
def test_abs_signed_zero(self, device, dtype):
# Both abs(0.0) and abs(-0.0) should result in 0.0
size = 128 + 1 # pick a large enough number with remainder so that
# both vectorized and nonvectorized op is tested
inp = torch.zeros(size, device=device, dtype=dtype)
inp[::2] = -0.0
inp = inp.abs()
for v in inp:
self.assertGreater(math.copysign(1.0, v), 0.0)
# TODO: update to compare against NumPy by rationalizing with OpInfo
@onlyCUDA
@dtypes(torch.float, torch.double)
def test_abs_zero(self, device, dtype):
# Both abs(0.0) and abs(-0.0) should result in 0.0
abs_zeros = torch.tensor([0.0, -0.0], device=device, dtype=dtype).abs().tolist()
for num in abs_zeros:
self.assertGreater(math.copysign(1.0, num), 0.0)
@dtypes(*get_all_fp_dtypes())
def test_isfinite_isinf_isnan(self, device, dtype):
vals = (-float('inf'), float('inf'), float('nan'), -1, 0, 1)
self.compare_with_numpy(torch.isfinite, np.isfinite, vals, device, dtype)
self.compare_with_numpy(torch.isinf, np.isinf, vals, device, dtype)
self.compare_with_numpy(torch.isnan, np.isnan, vals, device, dtype)
@dtypes(torch.int8, torch.int16, torch.int32, torch.int64)
def test_isfinite_isinf_isnan_int(self, device, dtype):
vals = (-1, 0, 1)
self.compare_with_numpy(torch.isfinite, np.isfinite, vals, device, dtype)
self.compare_with_numpy(torch.isinf, np.isinf, vals, device, dtype)
self.compare_with_numpy(torch.isnan, np.isnan, vals, device, dtype)
@dtypes(*(get_all_fp_dtypes()))
def test_isposinf_isneginf_float(self, device, dtype):
ops = ((torch.isposinf, np.isposinf), (torch.isneginf, np.isneginf))
vals = (-float('inf'), float('inf'), float('nan'), -1, 0, 1)
for torch_op, numpy_op in ops:
if torch_op == torch.isposinf:
target_vals = (0, 1, 0, 0, 0, 0)
else:
target_vals = (1, 0, 0, 0, 0, 0)
t = torch.tensor(vals, device=device, dtype=dtype)
# Manual check here as numpy does not support bfloat16
if dtype == torch.bfloat16:
self.assertEqual(torch_op(t),
torch.tensor(target_vals, device=device, dtype=torch.bool))
else:
self.compare_with_numpy(torch_op, numpy_op, vals, device, dtype)
# test the boolean tensor as the `out=` parameter
out = torch.empty_like(t, dtype=torch.bool)
t_target = torch.tensor(target_vals, device=device, dtype=torch.bool)
torch_op(t, out=out)
self.assertEqual(out, t_target)
@dtypes(*(get_all_int_dtypes() + [torch.bool]))
def test_isposinf_isneginf_int_and_bool(self, device, dtype):
ops = ((torch.isposinf, np.isposinf), (torch.isneginf, np.isneginf))
vals = (-1, 0, 1)
for torch_op, numpy_op in ops:
self.compare_with_numpy(torch_op, numpy_op, vals, device, dtype)
# test the boolean tensor as the `out=` parameter
t = torch.tensor(vals, device=device, dtype=dtype)
out = torch.empty_like(t, dtype=torch.bool)
t_target = torch.zeros_like(t, dtype=torch.bool)
torch_op(t, out=out)
self.assertEqual(out, t_target)
@dtypes(torch.complex64, torch.complex128)
def test_isposinf_isneginf_complex(self, device, dtype):
torch_ops = (torch.isposinf, torch.isneginf)
vals = (complex(0, float('inf')), complex(1, -float('inf')))
t = torch.tensor(vals, device=device, dtype=dtype)
out = torch.empty_like(t)
for torch_op in torch_ops:
with self.assertRaisesRegex(RuntimeError, 'does not support complex inputs'):
torch_op(t)
with self.assertRaisesRegex(RuntimeError, 'does not support complex inputs'):
torch_op(t, out=out)
@dtypes(*(get_all_dtypes(include_bool=False)))
def test_isposinf_isneginf_non_boolean_output(self, device, dtype):
# test non-boolean tensors as the `out=` parameters
# boolean outputs are tested in the above testcases
vals = (float('inf'), -float('inf'), 1.2)
t = torch.tensor(vals, device=device)
for torch_op in (torch.isposinf, torch.isneginf):
out = torch.empty_like(t, dtype=dtype)
with self.assertRaisesRegex(RuntimeError, 'does not support non-boolean outputs'):
torch_op(t, out=out)
@dtypes(torch.complex64, torch.complex128)
def test_isfinite_isinf_isnan_complex(self, device, dtype):
vals = (
complex(-float('inf'), float('inf')),
complex(-float('inf'), 0),
complex(0, float('inf')),
complex(float('inf'), float('nan')),
complex(float('nan'), 0),
complex(-1, 0),
complex(0, 1)
)
self.compare_with_numpy(torch.isfinite, np.isfinite, vals, device, dtype)
self.compare_with_numpy(torch.isinf, np.isinf, vals, device, dtype)
self.compare_with_numpy(torch.isnan, np.isnan, vals, device, dtype)
@dtypes(torch.complex64, torch.complex128)
def test_isreal_complex(self, device, dtype):
vals = (1, 1 + 1j, 2 + 0j, 3j, 2 - 1j, 2 - 0j)
self.compare_with_numpy(torch.isreal, np.isreal, vals, device, dtype)
@dtypes(*get_all_dtypes())
def test_isreal_noncomplex(self, device, dtype):
vals = (1, 2, 3)
# Manual check here since numpy doesn't support bfloat16
result = torch.isreal(torch.tensor(vals, dtype=dtype))
expected = torch.ones(result.size(), dtype=torch.bool, device=device)
self.assertEqual(result, expected)
@dtypes(torch.complex64)
def test_isreal_nan_inf(self, device, dtype):
vals = (
complex(-float('inf'), float('inf')),
complex(-float('inf'), 0),
complex(0, float('inf')),
complex(float('inf'), float('nan')),
complex(float('nan'), 0),
complex(-1, 0),
complex(0, 1)
)
self.compare_with_numpy(torch.isreal, np.isreal, vals, device, dtype)
@onlyCPU
def test_isfinite_type(self, device):
with self.assertRaises(TypeError):
torch.isfinite(1) # Parameter must be a tensor
@onlyCPU
def test_isinf_type(self, device):
with self.assertRaises(TypeError):
torch.isinf(1) # Parameter must be a tensor
def test_nonzero_empty(self, device):
def assert_tuple_empty(tup, dim):
self.assertEqual(dim, len(tup))
for t in tup:
self.assertEqual(torch.Size([0]), t.shape)
x = torch.randn(0, 2, 0, 5, 0, device=device)
y = torch.nonzero(x)
z = torch.nonzero(x, as_tuple=True)
self.assertEqual(0, y.numel())
self.assertEqual(torch.Size([0, 5]), y.shape)
assert_tuple_empty(z, 5)
x = torch.tensor(0.5, device=device)
y = torch.nonzero(x)
# nonzero with as_tuple returns a
# tuple of len 1 for a zero-dim tensor.
# This is done to match Numpy behavior.
z = torch.nonzero(x, as_tuple=True)
self.assertEqual(1, len(z))
self.assertEqual(torch.zeros(1, dtype=torch.long), z[0])
x = torch.zeros((), device=device)
y = torch.nonzero(x)
z = torch.nonzero(x, as_tuple=True)
self.assertEqual(torch.Size([0, 0]), y.shape)
self.assertEqual(1, len(z))
self.assertEqual(torch.empty(0, dtype=torch.long), z[0])
@dtypes(*get_all_dtypes())
def test_nonzero_noncontiguous(self, device, dtype):
x = make_tensor((10, 10, 10), dtype=dtype, device=device,
low=1, noncontiguous=False)
mask = make_tensor((10, 10, 10), dtype=torch.bool, device=device)
x[mask] = 0
def permute_storage(tensor, dims):
dest_dims = tuple(range(len(dims)))
return tensor.permute(dims).contiguous().movedim(dims, dest_dims)
# Assume contiguous case is correct
expect = x.nonzero()
# Dense, permuted
self.assertEqual(permute_storage(x, [0, 2, 1]).nonzero(), expect)
self.assertEqual(permute_storage(x, [2, 1, 0]).nonzero(), expect)
# Non-dense
nondense = torch.empty((40, 10, 20), dtype=dtype, device=device)[::4, :, ::2]
nondense[:] = x
self.assertEqual(nondense.nonzero(), expect)
# Non-dense, permuted
nondense = nondense.permute([0, 2, 1])
nondense[:] = x
self.assertEqual(nondense.nonzero(), expect)
# TODO: rationalize with exp OpInfo
@dtypes(*(get_all_fp_dtypes(include_half=False) +
get_all_complex_dtypes()))
@dtypesIfCUDA(*(get_all_fp_dtypes(include_half=True) +
get_all_complex_dtypes()))
def test_exp(self, device, dtype):
for v in (2, -2) + ((1j, 1 + 1j) if dtype.is_complex else ()):
a = torch.tensor(v, dtype=dtype, device=device) * torch.arange(18, device=device) / 3 * math.pi
a = a.to(dtype)
# bfloat16 overflows
if dtype == torch.bfloat16:
return
self.compare_with_numpy(torch.exp, np.exp, a)
if dtype.is_complex:
inf_real_zero_imag_in = torch.tensor(complex(float('inf'), 0), device=device, dtype=dtype)
inf_real_zero_imag_out = torch.exp(inf_real_zero_imag_in).item()
self.assertTrue(math.isinf(inf_real_zero_imag_out.real))
if self.device_type == 'cpu':
pass
# These are commented out because it cannot be consistently reproduced.
# This is incorrect. It should be zero. Need fix!
# https://github.com/pytorch/pytorch/issues/40590
# self.assertNotEqual(inf_real_zero_imag_out.imag, 0)
# This is incorrect. They should equal. Need fix!
# https://github.com/pytorch/pytorch/issues/40590
# with self.assertRaises(AssertionError):
# self.compare_with_numpy(torch.exp, np.exp, inf_real_zero_imag_in)
else:
self.assertEqual(inf_real_zero_imag_out.imag, 0, atol=0, rtol=0)
self.compare_with_numpy(torch.exp, np.exp, inf_real_zero_imag_in)
zero_real_inf_imag_in = torch.tensor(complex(0, float('inf')), device=device, dtype=dtype)
zero_real_inf_imag_out = torch.exp(zero_real_inf_imag_in).item()
self.assertTrue(math.isnan(zero_real_inf_imag_out.real))
self.assertTrue(math.isnan(zero_real_inf_imag_out.imag))
# Ensure we are notified when NumPy changes its behavior
self.compare_with_numpy(torch.exp, np.exp, zero_real_inf_imag_in)
inf_real_imag_in = torch.tensor(complex(float('inf'), float('inf')), device=device, dtype=dtype)
inf_real_imag_out = torch.exp(inf_real_imag_in).item()
if self.device_type == 'cpu':
pass
# This is incorrect. Need fix! https://github.com/pytorch/pytorch/issues/40590
# This is commented out because it cannot be consistently reproduced.
# with self.assertRaises(AssertionError):
# self.compare_with_numpy(torch.exp, np.exp, inf_real_imag_in)
else:
self.assertTrue(math.isinf(inf_real_imag_out.real))
self.assertTrue(math.isnan(inf_real_imag_out.imag))
self.compare_with_numpy(torch.exp, np.exp, inf_real_imag_in)
inf_real_nan_imag_in = torch.tensor(complex(float('inf'), float('nan')), device=device, dtype=dtype)
inf_real_nan_imag_out = torch.exp(inf_real_nan_imag_in).item()
if self.device_type == 'cpu':
pass
# This is incorrect. It should be inf. Need fix! https://github.com/pytorch/pytorch/issues/40590
# This is commented out because it cannot be consistently reproduced.
# with self.assertRaises(AssertionError):
# self.compare_with_numpy(torch.exp, np.exp, inf_real_nan_imag_in)
else:
self.assertTrue(math.isinf(inf_real_nan_imag_out.real))
self.assertTrue(math.isnan(inf_real_nan_imag_out.imag))
self.compare_with_numpy(torch.exp, np.exp, inf_real_nan_imag_in)
nan_real_inf_imag_in = torch.tensor(complex(float('nan'), float('inf')), device=device, dtype=dtype)
nan_real_inf_imag_out = torch.exp(nan_real_inf_imag_in).item()
self.assertTrue(math.isnan(nan_real_inf_imag_out.real))
self.assertTrue(math.isnan(nan_real_inf_imag_out.imag))
# Ensure we are notified when NumPy changes its behavior
self.compare_with_numpy(torch.exp, np.exp, nan_real_inf_imag_in)
instantiate_device_type_tests(TestUnaryUfuncs, globals())
if __name__ == '__main__':
run_tests()