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struct

Tensor

A tensor type designed to extend MAX Engine with custom ops.

Beware that this Tensor is completely different from the Tensor type in the Mojo standard library. Currently, this max.extensibility.Tensor is designed only for use when building custom ops for MAX Engine.

For example, here's how you can define a custom op with this Tensor:

from max.extensibility import Tensor, empty_tensor
from max import register
from math import erf, sqrt


@register.op("my_gelu")
fn gelu[type: DType, rank: Int](x: Tensor[type, rank]) -> Tensor[type, rank]:
    var output = empty_tensor[type](x.shape)

    @always_inline
    @parameter
    fn func[width: Int](i: StaticIntTuple[rank]) -> SIMD[type, width]:
        var tmp = x.simd_load[width](i)
        return tmp / 2 * (1 + erf(tmp / sqrt(2)))

    output.for_each[func]()
    return output^

Then, you must create a Mojo package with this op and load it with your model into MAX Engine. For more information, read about MAX extensibility.

Parameters​

  • ​type (DType): DType of the underlying data.
  • ​static_rank (Int): The tensor rank.

Fields​

  • ​data (DTypePointer[type, 0]):
  • ​shape (StaticIntTuple[static_rank]):
  • ​strides (StaticIntTuple[static_rank]):

Implemented traits​

AnyType, Stringable

Methods​

__init__​

__init__(inout self: Self, ptr: DTypePointer[type, 0], shape: StaticIntTuple[static_rank])

Constructs a new Tensor.

You usually should not instantiate a Tensor directly. Instead use empty_tensor().

Args:

  • ​ptr (DTypePointer[type, 0]): A pointer to the tensor data.
  • ​shape (StaticIntTuple[static_rank]): The shape of the tensor.

__init__(inout self: Self, ptr: DTypePointer[type, 0], shape: StaticIntTuple[static_rank], strides: StaticIntTuple[static_rank])

Constructs a new Tensor.

You usually should not instantiate a Tensor directly. Instead use empty_tensor().

Args:

  • ​ptr (DTypePointer[type, 0]): A pointer to the tensor data.
  • ​shape (StaticIntTuple[static_rank]): The shape of the tensor.
  • ​strides (StaticIntTuple[static_rank]): The stride size for each dimension.

__moveinit__​

__moveinit__(inout self: Self, owned existing: Self)

__del__​

__del__(owned self: Self)

nelems​

nelems(self: Self) -> Int

Gets the number of elements in the tensor.

rank​

rank(self: Self) -> Int

Gets the tensor rank.

store​

store[width: Int](inout self: Self, index: StaticIntTuple[static_rank], value: SIMD[type, width])

Stores multiple values at the specified indices in the tensor.

Parameters:

  • ​width (Int): The SIMD width.

Args:

  • ​index (StaticIntTuple[static_rank]): The indices where to store the values.
  • ​value (SIMD[type, width]): The values to store.

store[width: Int](inout self: Self, index: Int, value: SIMD[type, width])

Stores a single value at the specified index in the tensor.

Constraints:

The tensor's static_rank must be 1.

Parameters:

  • ​width (Int): The SIMD width.

Args:

  • ​index (Int): The index where to store the values.
  • ​value (SIMD[type, width]): The values to store.

get_nd_indices​

get_nd_indices(self: Self) -> StaticIntTuple[static_rank]

Creates empty indices with the same rank as this tensor.

simd_load​

simd_load[simd_width: Int](self: Self, index: Int) -> SIMD[type, $0]

Gets the values stored in the tensor at the given index.

Constraints:

The tensor's static_rank must be 1.

Parameters:

  • ​simd_width (Int): The SIMD width.

Args:

  • ​index (Int): The index where the values are stored.

Returns:

The values as a SIMD.

simd_load[simd_width: Int](self: Self, index: StaticIntTuple[static_rank]) -> SIMD[type, $0]

Gets the values stored in the tensor at the given indices.

Parameters:

  • ​simd_width (Int): The SIMD width.

Args:

  • ​index (StaticIntTuple[static_rank]): The indices where the values are stored.

Returns:

The values as a SIMD.

for_each​

for_each[func: fn[Int](StaticIntTuple[static_rank]) capturing -> SIMD[type, $0]](inout self: Self)

Executes a lambda for every element in the tensor.

The lambda function must take an Int parameter for the SIMD width, and a StaticIntTuple argument for tensor indices, and then return a SIMD with the mutated value at the given indeces.

Parameters:

  • ​func (fn[Int](StaticIntTuple[static_rank]) capturing -> SIMD[type, $0]): The lambda function to execute for each tensor indeces.