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 |