Type Promotion Rules

Array API specification for type promotion rules.

Type promotion rules can be understood at a high level from the following diagram:

Type promotion diagram

Type promotion diagram. Promotion between any two types is given by their join on this lattice. Only the types of participating arrays matter, not their values. Dashed lines indicate that behavior for Python scalars is undefined on overflow. Boolean, integer and floating-point dtypes are not connected, indicating mixed-kind promotion is undefined.

Rules

A conforming implementation of the array API standard must implement the following type promotion rules governing the common result type for two array operands during an arithmetic operation.

A conforming implementation of the array API standard may support additional type promotion rules beyond those described in this specification.

Note

Type codes are used here to keep tables readable; they are not part of the standard. In code, use the data type objects specified in Data Types (e.g., int16 rather than 'i2' ).

The following type promotion tables specify the casting behavior for operations involving two array operands. When more than two array operands participate, application of the promotion tables is associative (i.e., the result does not depend on operand order).

Signed integer type promotion table

i1 i2 i4 i8
i1 i1 i2 i4 i8
i2 i2 i2 i4 i8
i4 i4 i4 i4 i8
i8 i8 i8 i8 i8

where

  • i1 : 8-bit signed integer (i.e., int8 )

  • i2 : 16-bit signed integer (i.e., int16 )

  • i4 : 32-bit signed integer (i.e., int32 )

  • i8 : 64-bit signed integer (i.e., int64 )

Unsigned integer type promotion table

u1 u2 u4 u8
u1 u1 u2 u4 u8
u2 u2 u2 u4 u8
u4 u4 u4 u4 u8
u8 u8 u8 u8 u8

where

  • u1 : 8-bit unsigned integer (i.e., uint8 )

  • u2 : 16-bit unsigned integer (i.e., uint16 )

  • u4 : 32-bit unsigned integer (i.e., uint32 )

  • u8 : 64-bit unsigned integer (i.e., uint64 )

Mixed unsigned and signed integer type promotion table

u1 u2 u4
i1 i2 i4 i8
i2 i2 i4 i8
i4 i4 i4 i8
i8 i8 i8 i8

Floating-point type promotion table

f4 f8
f4 f4 f8
f8 f8 f8

where

  • f4 : single-precision (32-bit) floating-point number (i.e., float32 )

  • f8 : double-precision (64-bit) floating-point number (i.e., float64 )

Notes

  • Type promotion rules must apply when determining the common result type for two array operands during an arithmetic operation, regardless of array dimension. Accordingly, zero-dimensional arrays must be subject to the same type promotion rules as dimensional arrays.

  • Type promotion of non-numerical data types to numerical data types is unspecified (e.g., bool to intxx or floatxx ).

Note

Mixed integer and floating-point type promotion rules are not specified because behavior varies between implementations.

Mixing arrays with Python scalars

Using Python scalars (i.e., instances of bool , int , float ) together with arrays must be supported for:

  • array <op> scalar

  • scalar <op> array

where <op> is a built-in operator, including in-place operators (see Operators for operators supported by the array object) and scalar has a compatible type and value to the array dtype:

  • Python bool for a bool array dtype,

  • a Python int within the bounds of the given dtype for integer array dtypes,

  • a Python int or float for floating-point array dtypes

The expected behavior is then equivalent to:

  1. Convert the scalar to a 0-D array with the same dtype as that of the array used in the expression.

  2. Execute the operation for array <op> 0-D array (or 0-D array <op> array if scalar was the left-hand argument).

Note

Behaviour is not specified when mixing a Python float and an array with an integer dtype; this may give float32 , float64 , or raise an exception - behavior of implementations will differ.

The behavior is also not specified for integers outside of the bounds of a given integer dtype. It may overflow, or result in an error.