oneDNN/tests/benchdnn/doc/driver_reduction.md

Reduction Driver

Usage

    ./benchdnn --reduction [benchdnn-knobs] [reduction-knobs] [reduction-desc] ...

where reduction-knobs are:

• --sdt={f32 [default], bf16, f16, s8, u8, s32} -- src data type. Refer to data types for details.
• --ddt={f32 [default], bf16, f16, s8, u8, s32} -- dst data type. Refer to data types for details.
• --stag={nchw [default], ...} -- physical src memory layout. Refer to tags for details.
• --dtag={any [default], ...} -- physical dst memory layout. Refer to tags for details.
• --alg={sum [default], ...} -- algorithm for reduction operations. Refer to reduction primitive for details.
• --p=FLOAT -- float value corresponding to algorithm operation. Refer to Floating point arguments below.
• --eps=FLOAT -- float value corresponding to algorithm operation. Refer to Floating point arguments below.
• --match=REGEX -- skip problems not matching the regular expression in REGEX. By default no pattern is applied (run everything). Note: Windows may interpret only string arguments surrounded by double quotation marks.
• Any attributes options. Refer to attributes for details.

and reduction-desc is a problem descriptor. The canonical form is:

    NxNxNxNxN:NxNxNxNxN

where N is an integer number. This represents a 3D spatial problem with the following logical dimensions: N, C, D, H, W. Consider removing each xN from the end to specify fewer dimensions.

Floating point arguments

Some operations support p and eps arguments such as norm_lp_max, norm_lp_sum, norm_lp_power_p_max, norm_lp_power_p_sum.

Essence of Testing

Examples

Run the set of reduction primitive problems from inputs/reduction/shapes_ci with the default settings:

    ./benchdnn --reduction --batch=inputs/reduction/shapes_ci

Run a specific reduction primitive problem:

• Data type is f32 for source and destination tensors.
• Source tensor uses acb memory format.
• The reduce operation is sum.

    ./benchdnn --reduction --sdt=f32 --ddt=f32 --stag=acb --alg=sum 1x2x3:1x1x3

More examples with different driver options can be found at inputs/reduction/test_*. Examples with different problem descriptors can be found at inputs/reduction/shapes_*. Examples with different benchdnn common options can be found at driver_conv.md.