DType

A torch.DType is an object that represents the data type of a torch.Tensor. PyTorch has twelve different data types:

To find out if a torch.dtype is a floating point data type, the property is_floating_point can be used, which returns True if the data type is a floating point data type.

To find out if a torch.dtype is a complex data type, the property is_complex can be used, which returns True if the data type is a complex data type.

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.

[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