| .. currentmodule:: torch | |
| .. _tensor-attributes-doc: | |
| Tensor Attributes | |
| ================= | |
| Each ``torch.Tensor`` has a :class:`torch.dtype`, :class:`torch.device`, and :class:`torch.layout`. | |
| .. _dtype-doc: | |
| torch.dtype | |
| ----------- | |
| .. class:: torch.dtype | |
| A :class:`torch.dtype` is an object that represents the data type of a | |
| :class:`torch.Tensor`. PyTorch has twelve different data types: | |
| ========================== =========================================== =========================== | |
| Data type dtype Legacy Constructors | |
| ========================== =========================================== =========================== | |
| 32-bit floating point ``torch.float32`` or ``torch.float`` ``torch.*.FloatTensor`` | |
| 64-bit floating point ``torch.float64`` or ``torch.double`` ``torch.*.DoubleTensor`` | |
| 64-bit complex ``torch.complex64`` or ``torch.cfloat`` | |
| 128-bit complex ``torch.complex128`` or ``torch.cdouble`` | |
| 16-bit floating point [1]_ ``torch.float16`` or ``torch.half`` ``torch.*.HalfTensor`` | |
| 16-bit floating point [2]_ ``torch.bfloat16`` ``torch.*.BFloat16Tensor`` | |
| 8-bit integer (unsigned) ``torch.uint8`` ``torch.*.ByteTensor`` | |
| 8-bit integer (signed) ``torch.int8`` ``torch.*.CharTensor`` | |
| 16-bit integer (signed) ``torch.int16`` or ``torch.short`` ``torch.*.ShortTensor`` | |
| 32-bit integer (signed) ``torch.int32`` or ``torch.int`` ``torch.*.IntTensor`` | |
| 64-bit integer (signed) ``torch.int64`` or ``torch.long`` ``torch.*.LongTensor`` | |
| Boolean ``torch.bool`` ``torch.*.BoolTensor`` | |
| ========================== =========================================== =========================== | |
| .. [1] Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 | |
| significand bits. Useful when precision is important. | |
| .. [2] Sometimes referred to as Brain Floating Point: use 1 sign, 8 exponent and 7 | |
| significand bits. Useful when range is important, since it has the same | |
| number of exponent bits as ``float32`` | |
| To find out if a :class:`torch.dtype` is a floating point data type, the property :attr:`is_floating_point` | |
| can be used, which returns ``True`` if the data type is a floating point data type. | |
| To find out if a :class:`torch.dtype` is a complex data type, the property :attr:`is_complex` | |
| can be used, which returns ``True`` if the data type is a complex data type. | |
| .. _type-promotion-doc: | |
| When the dtypes of inputs to an arithmetic operation (`add`, `sub`, `div`, `mul`) differ, we promote | |
| by finding the minimum dtype that satisfies the following rules: | |
| * If the type of a scalar operand is of a higher category than tensor operands | |
| (where complex > floating > integral > boolean), we promote to a type with sufficient size to hold | |
| all scalar operands of that category. | |
| * If a zero-dimension tensor operand has a higher category than dimensioned operands, | |
| we promote to a type with sufficient size and category to hold all zero-dim tensor operands of | |
| that category. | |
| * If there are no higher-category zero-dim operands, we promote to a type with sufficient size | |
| and category to hold all dimensioned operands. | |
| A floating point scalar operand has dtype `torch.get_default_dtype()` and an integral | |
| non-boolean scalar operand has dtype `torch.int64`. Unlike numpy, we do not inspect | |
| values when determining the minimum `dtypes` of an operand. Quantized and complex types | |
| are not yet supported. | |
| Promotion Examples:: | |
| >>> float_tensor = torch.ones(1, dtype=torch.float) | |
| >>> double_tensor = torch.ones(1, dtype=torch.double) | |
| >>> complex_float_tensor = torch.ones(1, dtype=torch.complex64) | |
| >>> complex_double_tensor = torch.ones(1, dtype=torch.complex128) | |
| >>> int_tensor = torch.ones(1, dtype=torch.int) | |
| >>> long_tensor = torch.ones(1, dtype=torch.long) | |
| >>> uint_tensor = torch.ones(1, dtype=torch.uint8) | |
| >>> double_tensor = torch.ones(1, dtype=torch.double) | |
| >>> bool_tensor = torch.ones(1, dtype=torch.bool) | |
| # zero-dim tensors | |
| >>> long_zerodim = torch.tensor(1, dtype=torch.long) | |
| >>> int_zerodim = torch.tensor(1, dtype=torch.int) | |
| >>> torch.add(5, 5).dtype | |
| torch.int64 | |
| # 5 is an int64, but does not have higher category than int_tensor so is not considered. | |
| >>> (int_tensor + 5).dtype | |
| torch.int32 | |
| >>> (int_tensor + long_zerodim).dtype | |
| torch.int32 | |
| >>> (long_tensor + int_tensor).dtype | |
| torch.int64 | |
| >>> (bool_tensor + long_tensor).dtype | |
| torch.int64 | |
| >>> (bool_tensor + uint_tensor).dtype | |
| torch.uint8 | |
| >>> (float_tensor + double_tensor).dtype | |
| torch.float64 | |
| >>> (complex_float_tensor + complex_double_tensor).dtype | |
| torch.complex128 | |
| >>> (bool_tensor + int_tensor).dtype | |
| torch.int32 | |
| # Since long is a different kind than float, result dtype only needs to be large enough | |
| # to hold the float. | |
| >>> torch.add(long_tensor, float_tensor).dtype | |
| torch.float32 | |
| When the output tensor of an arithmetic operation is specified, we allow casting to its `dtype` except that: | |
| * An integral output tensor cannot accept a floating point tensor. | |
| * A boolean output tensor cannot accept a non-boolean tensor. | |
| * A non-complex output tensor cannot accept a complex tensor | |
| Casting Examples:: | |
| # allowed: | |
| >>> float_tensor *= double_tensor | |
| >>> float_tensor *= int_tensor | |
| >>> float_tensor *= uint_tensor | |
| >>> float_tensor *= bool_tensor | |
| >>> float_tensor *= double_tensor | |
| >>> int_tensor *= long_tensor | |
| >>> int_tensor *= uint_tensor | |
| >>> uint_tensor *= int_tensor | |
| # disallowed (RuntimeError: result type can't be cast to the desired output type): | |
| >>> int_tensor *= float_tensor | |
| >>> bool_tensor *= int_tensor | |
| >>> bool_tensor *= uint_tensor | |
| >>> float_tensor *= complex_float_tensor | |
| .. _device-doc: | |
| torch.device | |
| ------------ | |
| .. class:: torch.device | |
| A :class:`torch.device` is an object representing the device on which a :class:`torch.Tensor` is | |
| or will be allocated. | |
| The :class:`torch.device` contains a device type (``'cpu'`` or ``'cuda'``) and optional device | |
| ordinal for the device type. If the device ordinal is not present, this object will always represent | |
| the current device for the device type, even after :func:`torch.cuda.set_device()` is called; e.g., | |
| a :class:`torch.Tensor` constructed with device ``'cuda'`` is equivalent to ``'cuda:X'`` where X is | |
| the result of :func:`torch.cuda.current_device()`. | |
| A :class:`torch.Tensor`'s device can be accessed via the :attr:`Tensor.device` property. | |
| A :class:`torch.device` can be constructed via a string or via a string and device ordinal | |
| Via a string: | |
| :: | |
| >>> torch.device('cuda:0') | |
| device(type='cuda', index=0) | |
| >>> torch.device('cpu') | |
| device(type='cpu') | |
| >>> torch.device('cuda') # current cuda device | |
| device(type='cuda') | |
| Via a string and device ordinal: | |
| :: | |
| >>> torch.device('cuda', 0) | |
| device(type='cuda', index=0) | |
| >>> torch.device('cpu', 0) | |
| device(type='cpu', index=0) | |
| .. note:: | |
| The :class:`torch.device` argument in functions can generally be substituted with a string. | |
| This allows for fast prototyping of code. | |
| >>> # Example of a function that takes in a torch.device | |
| >>> cuda1 = torch.device('cuda:1') | |
| >>> torch.randn((2,3), device=cuda1) | |
| >>> # You can substitute the torch.device with a string | |
| >>> torch.randn((2,3), device='cuda:1') | |
| .. note:: | |
| For legacy reasons, a device can be constructed via a single device ordinal, which is treated | |
| as a cuda device. This matches :meth:`Tensor.get_device`, which returns an ordinal for cuda | |
| tensors and is not supported for cpu tensors. | |
| >>> torch.device(1) | |
| device(type='cuda', index=1) | |
| .. note:: | |
| Methods which take a device will generally accept a (properly formatted) string | |
| or (legacy) integer device ordinal, i.e. the following are all equivalent: | |
| >>> torch.randn((2,3), device=torch.device('cuda:1')) | |
| >>> torch.randn((2,3), device='cuda:1') | |
| >>> torch.randn((2,3), device=1) # legacy | |
| .. _layout-doc: | |
| torch.layout | |
| ------------ | |
| .. class:: torch.layout | |
| .. warning:: | |
| The ``torch.layout`` class is in beta and subject to change. | |
| A :class:`torch.layout` is an object that represents the memory layout of a | |
| :class:`torch.Tensor`. Currently, we support ``torch.strided`` (dense Tensors) | |
| and have beta support for ``torch.sparse_coo`` (sparse COO Tensors). | |
| ``torch.strided`` represents dense Tensors and is the memory layout that | |
| is most commonly used. Each strided tensor has an associated | |
| :class:`torch.Storage`, which holds its data. These tensors provide | |
| multi-dimensional, `strided <https://en.wikipedia.org/wiki/Stride_of_an_array>`_ | |
| view of a storage. Strides are a list of integers: the k-th stride | |
| represents the jump in the memory necessary to go from one element to the | |
| next one in the k-th dimension of the Tensor. This concept makes it possible | |
| to perform many tensor operations efficiently. | |
| Example:: | |
| >>> x = torch.Tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) | |
| >>> x.stride() | |
| (5, 1) | |
| >>> x.t().stride() | |
| (1, 5) | |
| For more information on ``torch.sparse_coo`` tensors, see :ref:`sparse-docs`. | |
| torch.memory_format | |
| ------------------- | |
| .. class:: torch.memory_format | |
| A :class:`torch.memory_format` is an object representing the memory format on which a :class:`torch.Tensor` is | |
| or will be allocated. | |
| Possible values are: | |
| - ``torch.contiguous_format``: | |
| Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in decreasing order. | |
| - ``torch.channels_last``: | |
| Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in | |
| ``strides[0] > strides[2] > strides[3] > strides[1] == 1`` aka NHWC order. | |
| - ``torch.preserve_format``: | |
| Used in functions like `clone` to preserve the memory format of the input tensor. If input tensor is | |
| allocated in dense non-overlapping memory, the output tensor strides will be copied from the input. | |
| Otherwise output strides will follow ``torch.contiguous_format`` |