Intel® oneAPI Deep Neural Network Developer Guide and Reference
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DynamicDequantize
General
DynamicDequantize operation converts a quantized (s8 or u8) tensor to a f32 tensor. It supports both per-tensor and per-channel asymmetric linear de-quantization. Rounding mode is library-implementation defined. Unlike the Dequantize, DynamicDequantize takes scales and zero-points as operator src tensors.
For per-tensor de-quantization
 
   For per-channel de-quantization, taking channel axis = 1 as an example:
 
   Operation attributes
Attribute Name  |  
        Description  |  
        Value Type  |  
        Supported Values  |  
        Required or Optional  |  
       
|---|---|---|---|---|
Specifies which de-quantization type is used.  |  
        string  |  
        per_tensor (default), per_channel  |  
        Optional  |  
       |
Specifies dimension on which per-channel de-quantization is applied.  |  
        s64  |  
        A s64 value in the range of [-r, r-1] where r = rank(src), 1 by default. Negative value means counting the dimension backwards from the end.  |  
        Optional  |  
       
Execution arguments
The inputs and outputs must be provided according to below index order when constructing an operation.
Inputs
Index  |  
        Argument Name  |  
        Required or Optional  |  
       
|---|---|---|
0  |  
        src  |  
        Required  |  
       
1  |  
        scales  |  
        Required  |  
       
2  |  
        zps  |  
        Optional  |  
       
Outputs
Index  |  
        Argument Name  |  
        Required or Optional  |  
       
|---|---|---|
0  |  
        dst  |  
        Required  |  
       
Supported data types
DynamicDequantize operation supports the following data type combinations.
Src  |  
        Dst  |  
        Scales  |  
        Zps  |  
       
|---|---|---|---|
s8  |  
        f32  |  
        f32  |  
        s8, u8, s32  |  
       
u8  |  
        f32  |  
        f32  |  
        s8, u8, s32  |