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QOrderedGelu

Description

Ordered Quantize Gelu.

 

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.

X (heterogeneous) – Q : object, N-dimensional input A.
scale_X (heterogeneous) – S : object, scale of the input A.
scale_Y (heterogeneous) – S : object, 3D scale of the output Y.

 Parameters : cluster,

order_X : enum, cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.
Default value “ORDER_COL”.
order_Y : enum, cublasLt order of matrix Y, must be same as order_X if specified together. Optional.
Default value “ORDER_COL”.
 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

 

 Y (heterogeneous) – Q : object, output of the Gelu.

Type Constraints

Q in (tensor(int8)) : Constrain input and output types to int8 tensors.

S in (tensor(float)) : Constrain scales to float32.

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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