Visible to Intel only — GUID: GUID-7F83E395-BD6B-4EB5-BAC6-94C3AFA36544
Visible to Intel only — GUID: GUID-7F83E395-BD6B-4EB5-BAC6-94C3AFA36544
Reduction
General
The reduction primitive performs reduction operation on arbitrary data. Each element in the destination is the result of reduction operation with specified algorithm along one or multiple source tensor dimensions:
where can be max, min, sum, mul, mean, Lp-norm and Lp-norm-power-p, is an index in an idle dimension and is an index in a reduction dimension.
Mean:
where is the size of a reduction dimension.
Lp-norm:
where can be max and sum.
Lp-norm-power-p:
where can be max and sum.
Notes
The reduction primitive requires the source and destination tensors to have the same number of dimensions.
Reduction dimensions are of size 1 in a destination tensor.
The reduction primitive does not have a notion of forward or backward propagations.
Execution Arguments
When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table.
Primitive input/output |
Execution argument index |
---|---|
DNNL_ARG_SRC |
|
DNNL_ARG_DST |
|
DNNL_ARG_ATTR_MULTIPLE_POST_OP(binary_post_op_position) | DNNL_ARG_SRC_1 |
Implementation Details
General Notes
The memory format can be either specified explicitly or by dnnl::memory::format_tag::any (recommended), in which case the primitive will derive the most appropriate memory format based on the format of the source tensor.
Post-Ops and Attributes
The following attributes are supported:
Data Types Support
The source and destination tensors may have f32, bf16, f16 or int8 data types. See Data Types page for more details.
Data Representation
Sources, Destination
The reduction primitive works with arbitrary data tensors. There is no special meaning associated with any of the dimensions of a tensor.
Implementation Limitations
Refer to Data Types for limitations related to data types support.
GPU
Only tensors of 6 or fewer dimensions are supported.
Performance Tips
Whenever possible, avoid specifying different memory formats for source and destination tensors.
Example
This C++ API example demonstrates how to create and execute a Reduction primitive.