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RestorePadding

Description

Restore paddings and fill padding with zeros. The input has padding with shape (total_tokens, hidden_size) and token_offset with shape (batch_size, sequence_length). The output has shape (batch_size, sequence_length, hidden_size).

 

Input parameters

 

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

 Graphs in : cluster, ONNX model architecture.

input (heterogeneous) – T : object, input tensor with shape (total_tokens, hidden_size).
token_offset (heterogeneous) – M : object, offset of non-padding tokens and paddings. Its shape is (batch_size, sequence_length).

 Parameters : cluster,

 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : 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 “1”.

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

Output parameters

output (heterogeneous) – T : object, output tensor with shape (batch_size, sequence_length, hidden_size).

Type Constraints

T in (tensor(float), tensor(float16)) : Constrain input and output types to float tensors.

M in (tensor(int32)) : Constrain token_offset to integer types.

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).
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