""" This module contains tensor creation utilities. """ import torch from typing import Optional, List, Tuple, Union, cast import math import collections.abc # Used by make_tensor for generating complex tensor. complex_to_corresponding_float_type_map = {torch.complex32: torch.float16, torch.complex64: torch.float32, torch.complex128: torch.float64} float_to_corresponding_complex_type_map = {v: k for k, v in complex_to_corresponding_float_type_map.items()} 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 ) -> 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``. 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. 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``. 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``. TypeError: If :attr:`dtype` isn't supported by this function. Examples: >>> 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) 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, high, lowest, highest, default_low, default_high, dtype): """ Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high) if required. """ def clamp(a, l, h): 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 # Checks for error cases if low != low or high != high: raise ValueError("make_tensor: one of low or high was NaN!") if low > high: raise ValueError("make_tensor: low must be weakly less than high!") low = clamp(low, lowest, highest) high = clamp(high, lowest, highest) if dtype in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]: return math.floor(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)) _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] if requires_grad and dtype not in _floating_types and dtype not in _complex_types: raise ValueError("make_tensor: requires_grad must be False for integral dtype") if dtype is torch.bool: result = torch.randint(0, 2, shape, device=device, dtype=dtype) # type: ignore[call-overload] elif dtype is torch.uint8: ranges = (torch.iinfo(dtype).min, torch.iinfo(dtype).max) low, high = cast(Tuple[int, int], _modify_low_high(low, high, ranges[0], ranges[1], 0, 10, dtype)) result = torch.randint(low, high, shape, device=device, dtype=dtype) # type: ignore[call-overload] elif dtype in _integral_types: ranges = (torch.iinfo(dtype).min, torch.iinfo(dtype).max) low, high = _modify_low_high(low, high, ranges[0], ranges[1], -9, 10, dtype) result = torch.randint(low, high, shape, device=device, dtype=dtype) # type: ignore[call-overload] elif dtype in _floating_types: ranges_floats = (torch.finfo(dtype).min, torch.finfo(dtype).max) low, high = _modify_low_high(low, high, ranges_floats[0], ranges_floats[1], -9, 9, dtype) rand_val = torch.rand(shape, device=device, dtype=dtype) result = high * rand_val + low * (1 - rand_val) elif dtype in _complex_types: float_dtype = complex_to_corresponding_float_type_map[dtype] ranges_floats = (torch.finfo(float_dtype).min, torch.finfo(float_dtype).max) low, high = _modify_low_high(low, high, ranges_floats[0], ranges_floats[1], -9, 9, dtype) real_rand_val = torch.rand(shape, device=device, dtype=float_dtype) imag_rand_val = torch.rand(shape, device=device, dtype=float_dtype) real = high * real_rand_val + low * (1 - real_rand_val) imag = high * imag_rand_val + low * (1 - imag_rand_val) result = torch.complex(real, imag) 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] if exclude_zero: if dtype in _integral_types or dtype is torch.bool: replace_with = torch.tensor(1, device=device, dtype=dtype) elif dtype in _floating_types: replace_with = torch.tensor(torch.finfo(dtype).tiny, device=device, dtype=dtype) else: # dtype in _complex_types: float_dtype = complex_to_corresponding_float_type_map[dtype] float_eps = torch.tensor(torch.finfo(float_dtype).tiny, device=device, dtype=float_dtype) replace_with = torch.complex(float_eps, float_eps) result[result == 0] = replace_with if dtype in _floating_types + _complex_types: result.requires_grad = requires_grad return result