Intel® oneAPI Deep Neural Network Developer Guide and Reference
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DynamicQuantize
General
DynamicQuantize operation converts a f32 tensor to a quantized (s8 or u8) tensor. It supports both per-tensor and per-channel asymmetric linear quantization. The target quantized data type is specified via the data type of dst logical tensor. Rounding mode is library-implementation defined.
For per-tensor quantization
For per-channel 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
DynamicQuantize operation supports the following data type combinations.
Src |
Scales |
Zps |
Dst |
|---|---|---|---|
f32 |
f32 |
s8, u8, s32 |
s8 |
f32 |
f32 |
s8, u8, s32 |
u8 |