StaticTranspose

Versioned name: StaticTranspose-1

Category: Movement

Short description: StaticTranspose operation reorders the input tensor dimensions. In StaticTranspose, order is given as an attribute. Use StaticTranspose if order is constant and available before runtime. Otherwise, use DynamicTranspose.

Detailed description: StaticTranspose operation reorders the input tensor dimensions. Source indices and destination indices are bound by the formula:

\[output[i(order[0]),\ i(order[1]),\ ...,\ i(order[N-1])]\ =\ input[i(0),\ i(1),\ ...,\ i(N-1)]\]

where:

\[i(j) \ in\ range\ 0...(input.shape[j]-1)\]

The input shape is [input.shape(0), input.shape(1), ……, input.shape(N-1)], the output shape is [input.shape(order[0]), input.shape(order[1]), …, input.shape(order[N-1])]. Output tensor may have a different memory layout with input tensor. StaticTranspose is not guaranteed to return a view or a copy when input tensor and output tensor can be inplaced, framework should not depend on this behavior.

Attributes:

• order

  • Description: order specifies the permutation to apply to the axes of the input shape. order must be a vector of integer numbers, with shape [N], where N is the rank of data. If an empty list [] is specified, then axes will be inverted to [N-1,…,1,0].

  • Range of values: integer in the range [-N, N-1]. Negative number means counting from last to the first axis.

  • Type: int64[]

  • Required: yes

Inputs:

• 1: input - the tensor to be Transposed. Required.

  • Type: T

Outputs

• 1: output - the output transposed tensor.

  • Type: T

Types

• T: f32, f16, bf16.

• Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.