# BNNSComputeNorm(_:_:_:_:) Computes the specified norm over an entire tensor or the specified axes. ``` func BNNSComputeNorm(_ dest: UnsafeMutablePointer, _ src: UnsafePointer, _ norm_type: BNNSNormType, _ axis_flags: UInt32) -> Int32 ``` ## Parameters `dest` The descriptor of the output. `src` The descriptor of the input. `norm_type` The type of the norm. This function supports only [`BNNSL2Norm`](/documentation/Accelerate/BNNSL2Norm). `axis_flags` The dimensions that the function uses to compute the norm. Set to `0` to specify that the function computes the norm over all dimensions. ## Discussion Use this function to compute the norm of either an entire tensor or the axis or axes of a tensor. For example, the following code defines a 3D tensor: ```swift let inputData = UnsafeMutableBufferPointer.allocate(capacity: 24) _ = inputData.initialize(from: [1, 2, 3, 4, 5, 6, 10, 20, 30, 40, 50, 60, 100, 200, 300, 400, 500, 600, 1000, 2000, 3000, 4000, 5000, 6000]) var inputDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutImageCHW, size: (3, 2, 4, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: inputData.baseAddress!, data_type: BNNSDataType.float, table_data: nil, table_data_type: BNNSDataType.float, data_scale: 1, data_bias: 0) ``` Define the `axis_flags` parameter as either `0b111` or `0` to specify that the operation computes the norm of the entire tensor. In this case, the norm is a scalar value, and the destination’s data layout must be a `BNNSDataLayoutVector` with a size of `1`. ```swift let outputData = UnsafeMutableBufferPointer.allocate(capacity: 1) var outputDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutVector, size: (1, 0, 0, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: outputData.baseAddress!, data_type: BNNSDataType.float, table_data: nil, table_data_type: BNNSDataType.float, data_scale: 1, data_bias: 0) BNNSComputeNorm(&outputDescriptor, &inputDescriptor, BNNSL2Norm, 0b111) // Prints `[9587.45]` print(Array(outputData)) ``` On return, the output descriptor contains a single value that is the square root of the sum of squares of each element in the tensor:  Specify an `axis_flags` of `0b100` to compute the norm along the second axis. In this case, the destination should be a matrix with a size that matches the zeroth and first dimensions of the source tensor: ```swift let outputData = UnsafeMutableBufferPointer.allocate(capacity: 6) var outputDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutColumnMajorMatrix, size: (3, 2, 0, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: outputData.baseAddress!, data_type: BNNSDataType.float, table_data: nil, table_data_type: BNNSDataType.float, data_scale: 1, data_bias: 0) BNNSComputeNorm(&outputDescriptor, &inputDescriptor, BNNSL2Norm, 0b100) // Prints // [1005.0378, 2010.075, 3015.113, // 4020.1511, 5025.189, 6030.227] print(Array(outputData)) ``` On return, the output descriptor contains six values that are the norms of the slices along the second axis of the input tensor:  To compute the norm along more that one dimension, define the destination tensor with a size of the dimensions you’re not calculating over. For example, the following code defines an `axis_flags` with a value of `0b101` to compute the norm of dimensions zero and two: ```swift let outputData = UnsafeMutableBufferPointer.allocate(capacity: 2) var outputDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutVector, size: (2, 0, 0, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: outputData.baseAddress!, data_type: BNNSDataType.float, table_data: nil, table_data_type: BNNSDataType.float, data_scale: 1, data_bias: 0) BNNSComputeNorm(&outputDescriptor, &inputDescriptor, BNNSL2Norm, 0b101) // Prints `[3760.507, 8819.171]` print(Array(outputData)) ``` On return, the output descriptor contains two values that are the norms of the top and bottom slices of the input tensor:  --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)