Source code for torch.testing._creation
"""
This module contains tensor creation utilities.
"""
import collections.abc
import math
import warnings
from typing import cast, List, Optional, Tuple, Union
import torch
_INTEGRAL_TYPES = [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]
_FLOATING_TYPES = [torch.float16, torch.bfloat16, torch.float32, torch.float64]
_COMPLEX_TYPES = [torch.complex32, torch.complex64, torch.complex128]
_BOOLEAN_OR_INTEGRAL_TYPES = [torch.bool, *_INTEGRAL_TYPES]
_FLOATING_OR_COMPLEX_TYPES = [*_FLOATING_TYPES, *_COMPLEX_TYPES]
def _uniform_random_(t: torch.Tensor, low: float, high: float) -> torch.Tensor:
# uniform_ requires to-from <= std::numeric_limits<scalar_t>::max()
# Work around this by scaling the range before and after the PRNG
if high - low >= torch.finfo(t.dtype).max:
return t.uniform_(low / 2, high / 2).mul_(2)
else:
return t.uniform_(low, high)
[docs]def make_tensor(
*shape: Union[int, torch.Size, List[int], Tuple[int, ...]],
dtype: torch.dtype,
device: Union[str, torch.device],
low: Optional[float] = None,
high: Optional[float] = None,
requires_grad: bool = False,
noncontiguous: bool = False,
exclude_zero: bool = False,
memory_format: Optional[torch.memory_format] = None,
) -> torch.Tensor:
r"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with
values uniformly drawn from ``[low, high)``.
If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable
finite values then they are clamped to the lowest or highest representable finite value, respectively.
If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,
which depend on :attr:`dtype`.
+---------------------------+------------+----------+
| ``dtype`` | ``low`` | ``high`` |
+===========================+============+==========+
| boolean type | ``0`` | ``2`` |
+---------------------------+------------+----------+
| unsigned integral type | ``0`` | ``10`` |
+---------------------------+------------+----------+
| signed integral types | ``-9`` | ``10`` |
+---------------------------+------------+----------+
| floating types | ``-9`` | ``9`` |
+---------------------------+------------+----------+
| complex types | ``-9`` | ``9`` |
+---------------------------+------------+----------+
Args:
shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor.
dtype (:class:`torch.dtype`): The data type of the returned tensor.
device (Union[str, torch.device]): The device of the returned tensor.
low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is
clamped to the least representable finite value of the given dtype. When ``None`` (default),
this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is
clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value
is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
.. deprecated:: 2.1
Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated
since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead.
requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``.
noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is
ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``.
exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value
depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating
point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the
:attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number
whose real and imaginary parts are both the smallest positive normal number representable by the complex
type. Default ``False``.
memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive
with ``noncontiguous``.
Raises:
ValueError: If ``requires_grad=True`` is passed for integral `dtype`
ValueError: If ``low >= high``.
ValueError: If either :attr:`low` or :attr:`high` is ``nan``.
ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed.
TypeError: If :attr:`dtype` isn't supported by this function.
Examples:
>>> # xdoctest: +SKIP
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
>>> from torch.testing import make_tensor
>>> # Creates a float tensor with values in [-1, 1)
>>> make_tensor((3,), device='cpu', dtype=torch.float32, low=-1, high=1)
>>> # xdoctest: +SKIP
tensor([ 0.1205, 0.2282, -0.6380])
>>> # Creates a bool tensor on CUDA
>>> make_tensor((2, 2), device='cuda', dtype=torch.bool)
tensor([[False, False],
[False, True]], device='cuda:0')
"""
def modify_low_high(
low: Optional[float],
high: Optional[float],
*,
lowest_inclusive: float,
highest_exclusive: float,
default_low: float,
default_high: float,
) -> Tuple[float, float]:
"""
Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high)
if required.
"""
def clamp(a: float, l: float, h: float) -> float:
return min(max(a, l), h)
low = low if low is not None else default_low
high = high if high is not None else default_high
if any(isinstance(value, float) and math.isnan(value) for value in [low, high]):
raise ValueError(
f"`low` and `high` cannot be NaN, but got {low=} and {high=}"
)
elif low == high and dtype in _FLOATING_OR_COMPLEX_TYPES:
warnings.warn(
"Passing `low==high` to `torch.testing.make_tensor` for floating or complex types "
"is deprecated since 2.1 and will be removed in 2.3. "
"Use torch.full(...) instead.",
FutureWarning,
)
elif low >= high:
raise ValueError(f"`low` must be less than `high`, but got {low} >= {high}")
elif high < lowest_inclusive or low >= highest_exclusive:
raise ValueError(
f"The value interval specified by `low` and `high` is [{low}, {high}), "
f"but {dtype} only supports [{lowest_inclusive}, {highest_exclusive})"
)
low = clamp(low, lowest_inclusive, highest_exclusive)
high = clamp(high, lowest_inclusive, highest_exclusive)
if dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
# 1. `low` is ceiled to avoid creating values smaller than `low` and thus outside the specified interval
# 2. Following the same reasoning as for 1., `high` should be floored. However, the higher bound of
# `torch.randint` is exclusive, and thus we need to ceil here as well.
return math.ceil(low), math.ceil(high)
return low, high
if len(shape) == 1 and isinstance(shape[0], collections.abc.Sequence):
shape = shape[0] # type: ignore[assignment]
shape = cast(Tuple[int, ...], tuple(shape))
if noncontiguous and memory_format is not None:
raise ValueError(
f"The parameters `noncontiguous` and `memory_format` are mutually exclusive, "
f"but got {noncontiguous=} and {memory_format=}"
)
if requires_grad and dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
raise ValueError(
f"`requires_grad=True` is not supported for boolean and integral dtypes, but got {dtype=}"
)
if dtype is torch.bool:
low, high = cast(
Tuple[int, int],
modify_low_high(
low,
high,
lowest_inclusive=0,
highest_exclusive=2,
default_low=0,
default_high=2,
),
)
result = torch.randint(low, high, shape, device=device, dtype=dtype)
elif dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
low, high = cast(
Tuple[int, int],
modify_low_high(
low,
high,
lowest_inclusive=torch.iinfo(dtype).min,
highest_exclusive=torch.iinfo(dtype).max
# In theory, `highest_exclusive` should always be the maximum value + 1. However, `torch.randint`
# internally converts the bounds to an int64 and would overflow. In other words: `torch.randint` cannot
# sample 2**63 - 1, i.e. the maximum value of `torch.int64` and we need to account for that here.
+ (1 if dtype is not torch.int64 else 0),
# This is incorrect for `torch.uint8`, but since we clamp to `lowest`, i.e. 0 for `torch.uint8`,
# _after_ we use the default value, we don't need to special case it here
default_low=-9,
default_high=10,
),
)
result = torch.randint(low, high, shape, device=device, dtype=dtype)
elif dtype in _FLOATING_OR_COMPLEX_TYPES:
low, high = modify_low_high(
low,
high,
lowest_inclusive=torch.finfo(dtype).min,
highest_exclusive=torch.finfo(dtype).max,
default_low=-9,
default_high=9,
)
result = torch.empty(shape, device=device, dtype=dtype)
_uniform_random_(
torch.view_as_real(result) if dtype in _COMPLEX_TYPES else result, low, high
)
else:
raise TypeError(
f"The requested dtype '{dtype}' is not supported by torch.testing.make_tensor()."
" To request support, file an issue at: https://github.com/pytorch/pytorch/issues"
)
if noncontiguous and result.numel() > 1:
result = torch.repeat_interleave(result, 2, dim=-1)
result = result[..., ::2]
elif memory_format is not None:
result = result.clone(memory_format=memory_format)
if exclude_zero:
result[result == 0] = (
1 if dtype in _BOOLEAN_OR_INTEGRAL_TYPES else torch.finfo(dtype).tiny
)
if dtype in _FLOATING_OR_COMPLEX_TYPES:
result.requires_grad = requires_grad
return result
