Versioned name: SquaredDifference-1

Category: Arithmetic

Short description: SquaredDifference performs element-wise subtraction operation with two given tensors applying multi-directional broadcast rules, after that each result of the subtraction is squared.

OpenVINO description: This OP is as same as OpenVINO OP


  • auto_broadcast

    • Description: specifies rules used for auto-broadcasting of input tensors.

    • Range of values:

      • none - no auto-broadcasting is allowed, all input shapes should match

      • numpy - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in ONNX docs.

    • Type: string

    • Default value: numpy

    • Required: no


  • 1: input_1 - the first input tensor. Required.

    • Type: T

  • 2: input_2 - the second input tensor. Required.

    • Type: T


  • 1: output - the output tensor of SquaredDifference operation. Required.

    • Type: T


  • 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.

Detailed description:

Before performing arithmetic operation, input_1 and input_2 are broadcasted if their shapes are different and auto_broadcast attributes is not none. Broadcasting is performed according to auto_broadcast value.

After broadcasting SquaredDifference does the following with the input tensors:

\[output_{i} = (input\_1_{i} - input\_2_{i}) ^ 2\]