oneDNN/tests/benchdnn/doc/driver_rnn.md

Recurrent Neural Network Driver

Usage

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

where rnn-knobs are:

• --prop={FWD_I [default], FWD_D, BWD_DW} -- dnnl_prop_kind_t. Refer to direction for details.
• --cfg={f32 [default], ...} -- refer to Configurations below.
• --tag={any:any:any [default], ...} -- physical src, weights and dst memory layouts. Refer to tags for details.
• --alg={VANILLA_RNN [default], VANILLA_LSTM, VANILLA_GRU, LBR_GRU, VANILLA_AUGRU, LBR_AUGRU} -- RNN algorithm.
• --direction={left2right [default], right2left, concat, sum} -- RNN evaluation direction.
• --activation={RELU [default], LOGISTIC, TANH} -- VANILLA_RNN activation functions.
• --scaling="scale_str" -- RNN scaling policy, default "" (no scaling).
• --scaling-proj="scale_str" -- RNN scaling policy for the projection weights, default "" (no scaling). Refer to attributes for details.
• --skip-nonlinear={true, false [default]} -- specify if transcendental activations will be treated as linear. This allows to test longer chains avoiding errors coming from non-linear activation functions. Especially relevant for int8 computations. For LSTM, GRU and AUGRU flows changes internal implementation since there is no external control over pre-defined activations in a cell.
• --trivial-strides={true, false [default]} -- specify if input tensors should have trivial strides or not. Each tensor stride is the product of previous dimensions.
• --with-peephole={true, false [default]} -- LSTM extension. Specify if LSTM with peephole should be run.
• --with-projection={true, false [default]} -- LSTM extension. Specify if LSTM with projection should be run.
• --l=INT -- override l (number of layers) value specified in the problem descriptor. When INT is set to 0 (the default), use l value specified in the problem descriptor.
• --t=INT -- override t (number of timestamps) value specified in the problem descriptor. When INT is set to 0 (the default), use t value specified in the problem descriptor.
• --mb=INT -- override mb (minibatch) value specified in the problem descriptor. When INT is set to 0 (the default), use mb value specified in the problem descriptor.
• --flags=[|O] -- RNN flags, default undef (no flags); where multiple simultaneous flags are supported. O is dnnl_rnn_flags_diff_weights_overwrite; Refer to RNN primitive for details.
• Any attributes options. Refer to attributes for details.

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

 lXtXmbX_sicX_slcX_dhcX_dicX_nS

Here X is an integer number and S is a string literal without spaces (n stands for name). The special symbol _ is ignored, so it may be used as a delimiter for better readability.

Description of RNN descriptor symbols:

• l is the number of layers. The default value is 1.
• t is the number of timestamps (or the sequence length). The default value is 1.
• mb is the minibatch. The default value is 2.
• sic is the feature size of src_iter. No default value.
• slc is the feature size of src_layer. The default value is sic.
• dhc is the hidden feature size. The default value is sic.
• dic is the feature size of dst_iter. The default value is dhc. For GRU and AUGRU it must be equal to dhc.

Precision Configurations

The --cfg option specifies the data types to be used for a problem. It also defines the data filling strategy. It is implicit for the integer type saturation. This option also defines the threshold for computation errors.

The table below shows supported name configurations for this driver:

states iter	states iter_c	input	dst_iter	dst_last_layer	bias	cfg	notes
f32	f32	f32	f32	f32	f32	f32	TBA
bf16	bf16	bf16	bf16	bf16	bf16	bf16	TBA
bf16	bf16	bf16	bf16	bf16	f32	bf16f32	TBA
u8	f32	u8	u8	u8	f32	u8u8u8u8	TBA
u8	f32	u8	u8	f32	f32	u8u8u8f32	TBA
f32	f32	u8	f32	u8	f32	f32u8f32u8	TBA
f32	f32	u8	f32	f32	f32	f32u8f32f32	TBA
f16	f16	f16	f16	f16	f16	f16	Only for GPU

Essence of Testing

TBA.

Examples. TBA.

Run the set of rnn training from an input file with the default settings:

    ./benchdnn --rnn --batch=inputs/rnn/shapes_training

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