mirror of
https://github.com/pytorch/pytorch.git
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df8bb5a42b
Summary: Reference: https://github.com/pytorch/pytorch/issues/42515 * OpInfo entry for Polygamma * Removes infra of torch_op_tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/51966 Reviewed By: bdhirsh Differential Revision: D27851858 Pulled By: mruberry fbshipit-source-id: 7f1d0273065e1df56a152f95a14513959af29a1b
1552 lines
71 KiB
Python
1552 lines
71 KiB
Python
import torch
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import numpy as np
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import warnings
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import math
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from itertools import product, chain
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from numbers import Number
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import random
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import unittest
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from torch._six import inf, nan
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from torch.testing._internal.common_utils import (
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TestCase, run_tests, torch_to_numpy_dtype_dict, numpy_to_torch_dtype_dict,
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suppress_warnings, make_tensor, TEST_SCIPY, slowTest, skipIfNoSciPy, IS_WINDOWS)
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from torch.testing._internal.common_methods_invocations import (
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unary_ufuncs, _NOTHING)
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from torch.testing._internal.common_device_type import (
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instantiate_device_type_tests, ops, dtypes, onlyCPU, onlyOnCPUAndCUDA,
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onlyCUDA, dtypesIfCUDA, precisionOverride, skipCUDAIfRocm, dtypesIfCPU,
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OpDTypes)
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from torch.testing import (
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floating_types_and, all_types_and_complex_and, floating_and_complex_types_and)
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if TEST_SCIPY:
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import scipy
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# Refer [scipy reference filter]
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# Filter operators for which the reference function
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# is available in the current environment (for reference_numerics tests).
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reference_filtered_ops = list(filter(lambda op: op.ref is not _NOTHING, unary_ufuncs))
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# Tests for unary "universal functions (ufuncs)" that accept a single
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# tensor and have common properties like:
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# - they are elementwise functions
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# - the input shape is the output shape
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# - they typically have method and inplace variants
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# - they typically support the out kwarg
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# - they typically have NumPy or SciPy references
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# See NumPy's universal function documentation
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# (https://numpy.org/doc/1.18/reference/ufuncs.html) for more details
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# about the concept of ufuncs.
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# Functions tested here:
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#
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# Interesting values and extremal values for different dtypes
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_unsigned_int_vals = (0, 1, 55, 127)
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_int_vals = (0, -1, 1, -55, 55, -127, 127, -128, 128)
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_large_int_vals = (-1113, 1113, -10701, 10701)
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_float_vals = (0.,
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-.001, .001,
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-.25, .25,
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-1., 1.,
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-math.pi / 2, math.pi / 2,
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-math.pi + .00001, math.pi - .00001,
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-math.pi, math.pi,
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-math.pi - .00001, math.pi + .00001)
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_large_float16_vals = (-501, 501,
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-1001.2, 1001.2,
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-13437.7, 13437.7)
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_large_float_vals = _large_float16_vals + (-4988429.2, 4988429.2, -1e20, 1e20)
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_float_extremals = (float('inf'), float('-inf'), float('nan'))
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_medium_length = 812
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_large_size = (1029, 917)
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# Returns generator of tensors of different sizes filled with values in domain
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# and with intested region filled with `vals`. This will help test different code
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# paths for the given vals
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def generate_tensors_from_vals(vals, device, dtype, domain):
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offset = 63
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assert _large_size[1] > (_medium_length + offset) # large tensor should be large enough
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assert len(vals) < _medium_length # medium tensor should contain all vals
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assert _medium_length % 4 == 0 # ensure vectorized code coverage
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# Constructs the large tensor containing vals
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large_tensor = make_tensor(_large_size, device=device, dtype=dtype, low=domain[0], high=domain[1])
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# Inserts the vals at an odd place
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large_tensor[57][offset:offset + len(vals)] = torch.tensor(vals, device=device, dtype=dtype)
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# Takes a medium sized copy of the large tensor containing vals
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medium_tensor = large_tensor[57][offset:offset + _medium_length]
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# Constructs scalar tensors
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scalar_tensors = (t.squeeze() for t in torch.split(medium_tensor, 1))
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# Tensors with no elements
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empty_sizes = ((0,), (0, 3, 3), (1, 0, 5), (6, 0, 0, 0), (3, 0, 1, 0))
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empty_tensors = (torch.empty(size, device=device, dtype=dtype) for size in empty_sizes)
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return chain(empty_tensors, scalar_tensors, (medium_tensor,), (large_tensor,))
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# [Note generate_numeric_tensors, generate_numeric_tensors_hard,
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# and generate_numeric_tensors_extremal]
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#
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# Returns an iterable of contiguous tensors with the same storage on the requested
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# device and with the requested dtype.
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#
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# This function is intended to test the non-vectorized and vectorized code
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# paths of unary functions, as well as their handling of odd tensor
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# sizes (like zero-dim tensors and tensors with zero elements).
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#
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# The iterable will include an empty tensor, tensors with no elements,
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# zero dim (scalar) tensors, small 1D tensors, a medium 1D tensor, and
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# a large 2D tensor.
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#
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# These tensors will include interesting values. The generate_numeric_tensors_hard
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# tests larger values (>500) and generate_numeric_tensors_extremal tests extremal
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# values like -inf, inf, and nan.
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#
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# The randomly generated values can be restricted by the domain
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# argument.
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def generate_numeric_tensors(device, dtype, *,
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domain=(None, None)):
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# Special-cases bool
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if dtype is torch.bool:
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tensors = (torch.empty(0, device=device, dtype=torch.bool),
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torch.tensor(True, device=device),
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torch.tensor(False, device=device),
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torch.tensor((True, False), device=device),
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make_tensor((_medium_length,), device=device, dtype=dtype, low=None, high=None),
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make_tensor(_large_size, device=device, dtype=dtype, low=None, high=None))
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return tensors
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# Acquires dtype-specific vals
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if dtype.is_floating_point or dtype.is_complex:
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vals = _float_vals
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# Converts float -> complex vals if dtype is complex
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if dtype.is_complex:
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vals = tuple(complex(x, y) for x, y in product(vals, vals))
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elif dtype is torch.uint8:
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vals = _unsigned_int_vals
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else: # dtypes is a signed integer type
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assert dtype in (torch.int8, torch.int16, torch.int32, torch.int64)
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vals = _int_vals
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return generate_tensors_from_vals(vals, device, dtype, domain)
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def generate_numeric_tensors_hard(device, dtype, *,
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domain=(None, None)):
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is_signed_integral = dtype in (torch.int8, torch.int16, torch.int32, torch.int64)
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if not (dtype.is_floating_point or dtype.is_complex or is_signed_integral):
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return ()
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if dtype.is_floating_point:
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if dtype is torch.float16:
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# float16 has smaller range.
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vals = _large_float16_vals
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else:
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vals = _large_float_vals
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elif dtype.is_complex:
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vals = tuple(complex(x, y) for x, y in chain(product(_large_float_vals, _large_float_vals),
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product(_float_vals, _large_float_vals),
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product(_large_float_vals, _float_vals)))
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else:
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vals = _large_int_vals
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return generate_tensors_from_vals(vals, device, dtype, domain)
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def generate_numeric_tensors_extremal(device, dtype, *,
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domain=(None, None)):
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if not (dtype.is_floating_point or dtype.is_complex):
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return ()
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vals = []
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if dtype.is_floating_point:
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vals = _float_extremals
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elif dtype.is_complex:
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vals = tuple(complex(x, y) for x, y in chain(product(_float_extremals, _float_extremals),
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product(_float_vals, _float_extremals),
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product(_float_extremals, _float_vals)))
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return generate_tensors_from_vals(vals, device, dtype, domain)
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# TODO: port test_unary_out_op_mem_overlap
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# TODO: add out= tests (different devices, dtypes, mismatched sizes,
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# correct sizes, 0 size, broadcasted out)
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# TODO: add test for inplace variants erroring on broadcasted inputs
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class TestUnaryUfuncs(TestCase):
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exact_dtype = True
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# Tests bool tensor negation raises the correct error
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def test_neg_error_message(self, device):
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msg = ("Negation, the `\\-` operator, on a bool tensor is not supported."
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" If you are trying to invert a mask, use the `\\~` or"
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" `logical_not\\(\\)` operator instead.")
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t = torch.tensor((False, True), device=device)
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with self.assertRaisesRegex(RuntimeError, msg):
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torch.neg(t)
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@dtypes(*floating_types_and(torch.bfloat16, torch.half))
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@ops((_fn for _fn in unary_ufuncs if _fn.domain != (None, None)))
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def test_float_domains(self, device, dtype, op):
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if not op.supports_dtype(dtype, torch.device(device).type):
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raise unittest.SkipTest('unsupported dtype')
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eps = (1e-5, 1e-3, 1e-1, 1, 2, 10, 20, 50, 100)
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low, high = op.domain
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# NOTE: the following two loops are separated for readability
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if low is not None:
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low_tensor = torch.tensor(low, device=device, dtype=dtype)
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for epsilon in eps:
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lower_tensor = low_tensor - epsilon
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# Skips the test if the difference is not representable,
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# which can occur if, for example, the difference is small
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# and the dtype is imprecise (like bfloat16 is)
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if lower_tensor.item() == low_tensor.item():
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continue
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result = op(lower_tensor)
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self.assertEqual(result.item(), float('nan'),
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msg=("input of {0} outside lower domain boundary"
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" {1} produced {2}, not nan!").format(lower_tensor.item(),
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low,
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result.item()))
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if high is not None:
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high_tensor = torch.tensor(high, device=device, dtype=dtype)
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for epsilon in eps:
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higher_tensor = high_tensor + epsilon
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# See above comment
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if higher_tensor.item() == high_tensor.item():
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continue
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result = op(higher_tensor)
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self.assertEqual(result.item(), float('nan'),
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msg=("input of {0} outside upper domain boundary"
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" {1} produced {2}, not nan!").format(higher_tensor.item(),
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high,
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result.item()))
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# Helper for comparing torch tensors and numpy arrays
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# TODO: should this or assertEqual also validate that strides are equal?
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def assertEqualHelper(self, actual, expected, msg, *, dtype, exact_dtype=True, **kwargs):
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assert isinstance(actual, torch.Tensor)
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# Some NumPy functions return scalars, not arrays
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if isinstance(expected, Number):
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self.assertEqual(actual.item(), expected, **kwargs)
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elif isinstance(expected, np.ndarray):
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# Handles exact dtype comparisons between arrays and tensors
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if exact_dtype:
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# Allows array dtype to be float32 when comparing with bfloat16 tensors
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# since NumPy doesn't support the bfloat16 dtype
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# Also ops like scipy.special.erf, scipy.special.erfc, etc, promote float16
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# to float32
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if expected.dtype == np.float32:
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assert actual.dtype in (torch.float16, torch.bfloat16, torch.float32)
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else:
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assert expected.dtype == torch_to_numpy_dtype_dict[actual.dtype]
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self.assertEqual(actual,
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torch.from_numpy(expected).to(actual.dtype),
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msg,
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exact_device=False,
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**kwargs)
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else:
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self.assertEqual(actual, expected, msg, exact_device=False, **kwargs)
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# Tests that the function and its (array-accepting) reference produce the same
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# values on given tensors
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def _test_reference_numerics(self, dtype, op, tensors, equal_nan=True):
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def _helper_reference_numerics(expected, actual, msg, exact_dtype, equal_nan=True):
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if not torch.can_cast(numpy_to_torch_dtype_dict[expected.dtype.type], dtype):
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exact_dtype = False
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if dtype in [torch.uint8, torch.int8, torch.bool]:
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# NOTE: For these dtypes, PyTorch computes in the default scalar type (float)
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# while NumPy computes in float16
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self.assertEqualHelper(actual, expected, msg, dtype=dtype,
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exact_dtype=exact_dtype, rtol=1e-3, atol=1e-2)
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else:
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self.assertEqualHelper(actual, expected, msg, dtype=dtype, equal_nan=equal_nan, exact_dtype=exact_dtype)
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for t in tensors:
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torch_kwargs, numpy_kwargs = op.sample_kwargs(t.device, dtype, t)
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if dtype is torch.bfloat16:
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a = t.cpu().to(torch.float32).numpy()
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else:
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a = t.cpu().numpy()
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actual = op(t, **torch_kwargs)
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expected = op.ref(a, **numpy_kwargs)
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# Crafts a custom error message for smaller, printable tensors
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if t.numel() < 10:
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msg = ("Failed to produce expected results! Input tensor was"
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" {0}, torch result is {1}, and reference result is"
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" {2}.").format(t, actual, expected)
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else:
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msg = None
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exact_dtype = True
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if isinstance(actual, torch.Tensor):
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_helper_reference_numerics(expected, actual, msg, exact_dtype, equal_nan)
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else:
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for x, y in zip(expected, actual):
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# testing multi-outputs results
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_helper_reference_numerics(x, y, msg, exact_dtype, equal_nan)
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# Tests that the function and its (array-accepting) reference produce the same
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# values on a range of tensors, including empty tensors, scalar tensors,
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# 1D tensors and a large 2D tensor with interesting and extremal values
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# and noncontiguities.
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@suppress_warnings
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@ops(reference_filtered_ops)
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def test_reference_numerics_normal(self, device, dtype, op):
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tensors = generate_numeric_tensors(device, dtype,
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domain=op.domain)
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self._test_reference_numerics(dtype, op, tensors)
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@suppress_warnings
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@ops(reference_filtered_ops, allowed_dtypes=floating_and_complex_types_and(
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torch.bfloat16, torch.half, torch.int8, torch.int16, torch.int32, torch.int64
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))
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def test_reference_numerics_hard(self, device, dtype, op):
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if not op.handles_large_floats:
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raise self.skipTest("This op does not handle large values")
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tensors = generate_numeric_tensors_hard(device, dtype,
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domain=op.domain)
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self._test_reference_numerics(dtype, op, tensors)
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@suppress_warnings
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@ops(reference_filtered_ops,
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allowed_dtypes=floating_and_complex_types_and(torch.bfloat16, torch.half))
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def test_reference_numerics_extremal(self, device, dtype, op):
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handles_extremals = (op.handles_complex_extremals if
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dtype in (torch.cfloat, torch.cdouble) else op.handles_extremals)
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if not handles_extremals:
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raise self.skipTest("This op does not handle extremal values")
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tensors = generate_numeric_tensors_extremal(device, dtype,
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domain=op.domain)
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# https://github.com/pytorch/pytorch/issues/50749
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equal_nan = "relaxed" if device.startswith('cuda') else True
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self._test_reference_numerics(dtype, op, tensors, equal_nan)
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# Tests for testing (non)contiguity consistency
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@ops(unary_ufuncs)
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def test_contig_vs_every_other(self, device, dtype, op):
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contig = make_tensor((1026,), device=device, dtype=dtype,
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low=op.domain[0], high=op.domain[1])
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non_contig = contig[::2]
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self.assertTrue(contig.is_contiguous())
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self.assertFalse(non_contig.is_contiguous())
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torch_kwargs, _ = op.sample_kwargs(device, dtype, non_contig)
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self.assertEqual(op(contig, **torch_kwargs)[::2], op(non_contig, **torch_kwargs))
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@ops(unary_ufuncs)
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def test_contig_vs_transposed(self, device, dtype, op):
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contig = make_tensor((789, 357), device=device, dtype=dtype,
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low=op.domain[0], high=op.domain[1])
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non_contig = contig.T
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self.assertTrue(contig.is_contiguous())
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self.assertFalse(non_contig.is_contiguous())
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torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
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self.assertEqual(op(contig, **torch_kwargs).T, op(non_contig, **torch_kwargs))
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@ops(unary_ufuncs)
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def test_non_contig(self, device, dtype, op):
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shapes = [(5, 7), (1024,)]
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for shape in shapes:
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contig = make_tensor(shape, device, dtype,
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low=op.domain[0], high=op.domain[1])
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non_contig = torch.empty(shape + (2,), device=device, dtype=dtype)[..., 0]
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non_contig.copy_(contig)
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self.assertTrue(contig.is_contiguous())
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self.assertFalse(non_contig.is_contiguous())
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torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
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self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs))
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@ops(unary_ufuncs)
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def test_non_contig_index(self, device, dtype, op):
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contig = make_tensor((2, 2, 1, 2), device, dtype,
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low=op.domain[0], high=op.domain[1])
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non_contig = contig[:, 1, ...]
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contig = non_contig.contiguous()
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self.assertTrue(contig.is_contiguous())
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self.assertFalse(non_contig.is_contiguous())
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torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
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self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs))
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@ops(unary_ufuncs)
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def test_non_contig_expand(self, device, dtype, op):
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shapes = [(1, 3), (1, 7), (5, 7)]
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for shape in shapes:
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contig = make_tensor(shape, device, dtype,
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low=op.domain[0], high=op.domain[1])
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non_contig = contig.clone().expand(3, -1, -1)
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self.assertTrue(contig.is_contiguous())
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self.assertFalse(non_contig.is_contiguous())
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torch_kwargs, _ = op.sample_kwargs(device, dtype, contig)
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contig = op(contig, **torch_kwargs)
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non_contig = op(non_contig, **torch_kwargs)
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for i in range(3):
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self.assertEqual(contig, non_contig[i],
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msg='non-contiguous expand[' + str(i) + ']')
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@ops(unary_ufuncs)
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def test_contig_size1(self, device, dtype, op):
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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(*(torch.testing.get_all_int_dtypes() + [torch.bool] +
|
|
torch.testing.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.cfloat, 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
|
|
# TODO: Add value `-1931.99999994`, to the tensor below when
|
|
# https://github.com/pytorch/pytorch/issues/49015 is fixed
|
|
tensor = torch.tensor([-0.999999994, -1.999999994, -2.0000000111,
|
|
-100.99999994, 0.000000111,
|
|
-0.000000111, 0, -0, -1, -2, -931], dtype=dtype, device=device)
|
|
self.compare_with_numpy(torch.digamma, scipy.special.digamma, tensor)
|
|
|
|
@skipCUDAIfRocm
|
|
@dtypes(*torch.testing.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(*torch.testing.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 = torch.testing.get_all_int_dtypes() + \
|
|
torch.testing.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 torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=False):
|
|
input = make_tensor((50, 50), device, dtype)
|
|
|
|
dtypes = list(torch.testing.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))
|
|
|
|
# TODO opinfo mvlgamma
|
|
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
|
|
def test_mvlgamma(self, device):
|
|
from scipy.special import multigammaln
|
|
for d in range(1, 5):
|
|
input = torch.empty(10, device=device).uniform_(d, 10)
|
|
res_torch = torch.mvlgamma(input, d)
|
|
res_scipy = multigammaln(input.cpu().numpy(), d)
|
|
self.assertEqual(res_torch.cpu().numpy(), res_scipy, atol=1e-5, rtol=0)
|
|
|
|
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: review with ceil opinfo
|
|
@onlyCUDA
|
|
def test_ceil_out_mismatch(self, device):
|
|
a = torch.randn(1)
|
|
b = torch.randn(1, device=device)
|
|
self.assertRaises(RuntimeError, lambda: torch.ceil(a, out=b))
|
|
|
|
# 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)
|
|
|
|
# do ops like threshold need a test_unary(_nonufunc) test suite?
|
|
@onlyCPU
|
|
@dtypes(*torch.testing.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(*torch.testing.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(*torch.testing.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(*torch.testing.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(*torch.testing.get_all_fp_dtypes())
|
|
@dtypes(torch.bfloat16, torch.float32, torch.float64)
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
def test_special_i0e_vs_scipy(self, device, dtype):
|
|
def check_equal(t):
|
|
# Test by comparing to scipy
|
|
actual = torch.special.i0e(t)
|
|
if dtype is torch.bfloat16:
|
|
t = t.to(torch.float32)
|
|
expected = scipy.special.i0e(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)
|
|
|
|
range = (-1e7, 1e7)
|
|
if dtype == torch.half:
|
|
range = (-65000, 65000)
|
|
|
|
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: rationalize with abs testing and verify absolute is tested as an alias
|
|
@dtypes(torch.float)
|
|
def test_absolute(self, device, dtype):
|
|
# absolute is an alias for abs. Just check to see that results
|
|
# are the same.
|
|
t = torch.randn(10, 10, device=device, dtype=dtype)
|
|
r_abs = t.abs()
|
|
r_absolute = t.absolute()
|
|
self.assertEqual(r_abs, r_absolute)
|
|
|
|
r_abs = torch.abs(t)
|
|
r_absolute = torch.absolute(t)
|
|
self.assertEqual(r_abs, r_absolute)
|
|
|
|
r_abs = torch.empty((10, 10), device=device, dtype=dtype)
|
|
r_absolute = torch.empty((10, 10), device=device, dtype=dtype)
|
|
torch.abs(t, out=r_abs)
|
|
torch.absolute(t, out=r_absolute)
|
|
self.assertEqual(r_abs, r_absolute)
|
|
|
|
from copy import deepcopy
|
|
t_copy = deepcopy(t)
|
|
t.absolute_()
|
|
t_copy.abs_()
|
|
self.assertEqual(t, t_copy)
|
|
|
|
# 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(*torch.testing.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(*(torch.testing.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(*(torch.testing.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(*(torch.testing.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(*torch.testing.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])
|
|
|
|
# TODO: rationalize with exp OpInfo
|
|
@dtypes(*(torch.testing.get_all_fp_dtypes(include_half=False) +
|
|
torch.testing.get_all_complex_dtypes()))
|
|
@dtypesIfCUDA(*(torch.testing.get_all_fp_dtypes(include_half=True) +
|
|
torch.testing.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)
|
|
if dtype == torch.bfloat16:
|
|
with self.assertRaises(TypeError): # compare_with_numpy doesn't support bfloat16
|
|
self.compare_with_numpy(torch.exp, np.exp, a)
|
|
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)
|
|
|
|
# This function tests that a nan value is returned for input values not in domain
|
|
@dtypes(torch.float32, torch.float64)
|
|
def test_acosh_domain_float(self, device, dtype):
|
|
# Domain of acosh is [1, inf), for values outside the domain - output is mapped
|
|
# to NaN, except for input value `inf` - output is mapped to `inf`
|
|
sample = torch.tensor([float('-inf'), 1.00, -1.23, -0.06, 0.98, float('inf')],
|
|
device=device, dtype=dtype)
|
|
nan_mask = torch.tensor([True, False, True, True, True, False], device=device)
|
|
inf_mask = torch.tensor([False, False, False, False, False, True], device=device)
|
|
self.assertEqual(torch.isnan(torch.acosh(sample)), nan_mask)
|
|
self.assertEqual(torch.isnan(sample.acosh()), nan_mask)
|
|
self.assertEqual(torch.isinf(torch.acosh(sample)), inf_mask)
|
|
self.assertEqual(torch.isinf(sample.acosh()), inf_mask)
|
|
|
|
# This function tests that a nan value is returned for input values not in domain
|
|
@dtypes(torch.float32, torch.float64)
|
|
def test_atanh_domain_float(self, device, dtype):
|
|
# Domain of atanh is (-1, 1), for edge values (-1 and 1) - output is mapped
|
|
# to inf and for other values outside this range - output is mapped to NaN
|
|
sample = torch.tensor([float('-inf'), -1.00, 1.00, -1.23, 1.06, float('inf')],
|
|
device=device, dtype=dtype)
|
|
nan_mask = torch.tensor([True, False, False, True, True, True], device=device)
|
|
inf_mask = torch.tensor([False, True, True, False, False, False], device=device)
|
|
# For values not in domain (except -1.0 and 1.0), atanh should return nan
|
|
self.assertEqual(torch.isnan(torch.atanh(sample)), nan_mask)
|
|
self.assertEqual(torch.isnan(sample.atanh()), nan_mask)
|
|
# For values -1.0 and 1.0, atanh should return -inf and inf respectively
|
|
self.assertEqual(torch.isinf(torch.atanh(sample)), inf_mask)
|
|
self.assertEqual(torch.isinf(sample.atanh()), inf_mask)
|
|
|
|
|
|
instantiate_device_type_tests(TestUnaryUfuncs, globals())
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|