DynamicDequantize

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 3/31/2025

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DynamicDequantize

General

The Dynamic Dequantize operation converts a quantized (s4, u4, s8, or u8) tensor to an bf16, f16 or f32 tensor. It supports per-tensor, per-channel, and per-group asymmetric linear de-quantization. The rounding mode is defined by the library implementation. Unlike the Dequantize, Dynamic Dequantize 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:

For per-group de-quantization, let’s take group shape = Gx1 as an example. It indicates that one scaling factor will de adopted for G elements in the src tensor. On the dimensions where group quantization is adopted, make channelNum equal to the dimension of src and groupNum equal to channelNum/group size:

Where:

On other dimensions:

Operation attributes

Attribute Name	Description	Value Type	Supported Values	Required or Optional
qtype	Specifies which de-quantization type is used.	string	`per_tensor` (default), `per_channel`	Optional
axis	Specifies dimension on which per-channel de-quantization is applied.	s64	An s64 value in the range of [-r, r-1] where r = rank(src), `1` by default. Negative values mean counting the dimension backwards from the end.	Optional
group_shape	Specifies the group shape of an operation.	s64	An s64 list indicates the group size on the dimensions where grouped quantization is adopted.	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

NOTE:

scales is a bf16/f16/f32 tensor to be applied to the de-quantization formula. For qtype = per-tensor, there should be only one element in the scales tensor. For qtype = per-channel, the element number should be equal to the element number of the src tensor along the dimension axis. For qtype = per-gropup, the scale tensor should have the same number of dimension as the src tensor. On the dimensions where grouped quantization is applied, the dimension should be the number of groups, which equals to src_dim / group_size, while other dimensions should match the src tensor.

NOTE:

zps is a tensor with offset values that map to zero. For qtype = per-tensor, there should be only one element in the zps tensor. For qtype = per-channel, the element number should be equal to the element number of input tensor along the dimension axis. For qtype = per-group, the zps tensor should have the same number of dimensions as the src tensor. On the dimensions where grouped quantization is applied, the dimension should be the number of groups, which equals to src_dim / group_size, while other dimensions should match the src tensor. If omitted, the zps values are assumed to be zero.

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	f16, bf16, f32	f16, bf16, f32	s8, u8, s32
u8	f16, bf16, f32	f16, bf16, f32	s8, u8, s32
s4	f16, bf16, f32	f16, bf16, f32	s4, u4, s32
u4	f16, bf16, f32	f16, bf16, f32	s4, u4, s32

It’s expected that the data types of scales and dst should be the same.

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

DynamicDequantize

General

Operation attributes

Execution arguments

Supported data types