Forum
Login
Register

Welcome to our Support Center
UpdatedAugust 12, 2025

BitmaskBiasDropout

Description

output, dropout_bitmask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.

 

Input parameters

 

specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.

Β Graphs in :Β cluster, ONNX model architecture.

data – T : object, the input data as tensor.
bias – T : object, the bias input, a vector with the same shape as last dim of data OR same shape with data.
residual – T : object, the residual input, must have the same shape as data.
ratio – T1 : object, the ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it’s non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
training mode – T2 : object, if set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.

Β Parameters :Β cluster,

seed : integer, seed to the random generator, if not specified we will auto generate one.
Default value β€œ0”.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β€œ1”.

Β name (optional) :Β string,Β name of the node.

Output parameters

Β Graphs out :Β cluster, ONNX model architecture.

output – T : object, the output.
mask – T3 : object, the output mask of dropout.

Type Constraints

TΒ in (tensor(bfloat16),Β tensor(double),Β tensor(float),Β tensor(float16)) : Constrain input and output types to float tensors.

T1Β in (tensor(bfloat16),Β tensor(double),Β tensor(float),Β tensor(float16)) : Constrain input ‘ratio’ types to float tensors.

T2Β in (tensor(bool)) : Constrain input ‘training_mode’ types to boolean tensors.

T3Β in (tensor(uint32)) : Constrain output ‘mask’ types to uint32 tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
Table of Contents