Python module

dtype

Provides data type definitions for tensors in MAX Engine. These data types are essential for defining the precision and memory layout of tensor data when working with machine learning models.

This module defines the DType enum, which represents all supported tensor data types in MAX Engine, including:

Integer types (signed and unsigned): int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64
Floating-point types: float8 variants | float16 | bfloat16 | float32 | float64
Boolean type

The module also provides utilities for converting between MAX Engine data types and NumPy dtypes, making it easy to interoperate with the NumPy ecosystem.

import numpy as np
from max.dtype import DType

tensor = np.zeros((2, 3), dtype=DType.float32.to_numpy())

# Convert NumPy dtype to MAX DType
array = np.ones((4, 4), dtype=np.float16)
max_dtype = DType.from_numpy(array.dtype)

# Check properties of data types
is_float = DType.float32.is_float()  # True
is_int = DType.int64.is_integral()   # True
size = DType.float64.size_in_bytes   # 8
import numpy as np
from max.dtype import DType

tensor = np.zeros((2, 3), dtype=DType.float32.to_numpy())

# Convert NumPy dtype to MAX DType
array = np.ones((4, 4), dtype=np.float16)
max_dtype = DType.from_numpy(array.dtype)

# Check properties of data types
is_float = DType.float32.is_float()  # True
is_int = DType.int64.is_integral()   # True
size = DType.float64.size_in_bytes   # 8

`DType`

class max.dtype.DType(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

The tensor data type.

`align`

property align

Returns the alignment requirement of the data type in bytes.

The alignment specifies the memory boundary that values of this data type must be aligned to for optimal performance and correctness.

`bfloat16`

bfloat16 = 71

`bool`

bool = 1

`float16`

float16 = 70

`float32`

float32 = 72

`float64`

float64 = 73

`float8_e4m3fn`

float8_e4m3fn = 66

`float8_e4m3fnuz`

float8_e4m3fnuz = 67

`float8_e5m2`

float8_e5m2 = 68

`float8_e5m2fnuz`

float8_e5m2fnuz = 69

`from_numpy()`

from_numpy()

Converts a NumPy dtype to the corresponding DType.

Parameters:: dtype (np.dtype ) – The NumPy dtype to convert.
Returns:: The corresponding DType enum value.
Return type:: DType
Raises:: ValueError – If the input dtype is not supported.

`from_torch()`

from_torch()

Parameters:: dtype (dtype )
Return type:: DType

`int16`

int16 = 137

`int32`

int32 = 139

`int64`

int64 = 141

`int8`

int8 = 135

`is_float`

is_float

Checks if the data type is a floating-point type.

`is_float8`

is_float8

Checks if the data type is an 8-bit floating-point type.

`is_half`

is_half

Checks if the data type is a half-precision floating-point type.

`is_integral`

is_integral

Checks if the data type is an integer type.

`is_signed_integral`

is_signed_integral

Checks if the data type is a signed integer type.

`is_unsigned_integral`

is_unsigned_integral

Checks if the data type is an unsigned integer type.

`size_in_bytes`

property size_in_bytes

Returns the size of the data type in bytes.

This indicates how many bytes are required to store a single value of this data type in memory.

`to_numpy()`

to_numpy()

Converts this DType to the corresponding NumPy dtype.

Returns:: The corresponding NumPy dtype object.
Return type:: DType
Raises:: ValueError – If the dtype is not supported.

`to_torch()`

to_torch()

Parameters:: dtype (DType )
Return type:: dtype

`uint16`

uint16 = 136

`uint32`

uint32 = 138

`uint64`

uint64 = 140

`uint8`

uint8 = 134

DType​

align​

bfloat16​

bool​

float16​

float32​

float64​

float8_e4m3fn​

float8_e4m3fnuz​

float8_e5m2​

float8_e5m2fnuz​

from_numpy()​

from_torch()​

int16​

int32​

int64​

int8​

is_float​

is_float8​

is_half​

is_integral​

is_signed_integral​

is_unsigned_integral​

size_in_bytes​

to_numpy()​

to_torch()​

uint16​

uint32​

uint64​

uint8​