Versioned name: StaticReshape-1

Category: Shape manipulation

Short description: StaticReshape changes dimensions of the input tensor according to the specified shape. Input tensor volume is equal to output tensor volume, where volume is the product of dimensions. Output tensor may have a different memory layout from input tensor. StaticReshape is not guaranteed to return a view or a copy when input tensor and output tensor can be inplaced, user should not depend on this behavior. In StaticReshape, shape is given as an attribute. Use StaticReshape if shape is constant and available before runtime. Otherwise, use DynamicReshape.


  • shape

    • Description: shape is an array specifies the output shape. shape[i] gives the lengths of the i-th dimension of output. -1 means that this dimension is calculated to keep the overall elements count the same as in the input tensor. No more than one -1 can be used in a StaticReshape operation.

    • Range of values: values >= -1

    • Type: int64[]

    • Default value: None

    • Required: yes

  • special_zero

    • Description: special_zero controls how zero values in shape are interpreted. If special_zero is false, then 0 is interpreted as-is which means that output shape will contain a zero dimension at the specified location. Input and output tensors are empty in this case. If special_zero is true, then all zeros in shape implies the copying of corresponding dimensions from data.shape into the output shape. If special_zero is false, shape should not contain both 0 and -1 in the same time. Because target shape can’t be infered in this situation. Shape inference begins from leftmost(i.e. shape[0]) to the rightmost(i.e. shape[n-1]). For example, data shape is (3, 4, 5), shape is (0, -1), when special_zero is true, target shape is inferred as (3, 20).

    • Range of values: false or true

    • Type: boolean

    • Default value: None

    • Required: yes


  • 1: data – multidimensional input tensor of type T. Required.

    • Type: T


  • 1: Output tensor with the same content as data but with shape defined by attribute shape.

    • Type: T


  • T: f32,f16,bf16