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# Fast RNN benchmarks
Benchmarks for TorchScript models
For most stable results, do the following:
- Set CPU Governor to performance mode (as opposed to energy save)
- Turn off turbo for all CPUs (assuming Intel CPUs)
- Shield cpus via `cset shield` when running benchmarks.
Some of these scripts accept command line args but most of them do not because
I was lazy. They will probably be added sometime in the future, but the default
sizes are pretty reasonable.
## Test fastrnns (fwd + bwd) correctness
Test the fastrnns benchmarking scripts with the following:
`python -m fastrnns.test`
or run the test independently:
`python -m fastrnns.test --rnns jit`
## Run benchmarks
`python -m fastrnns.bench`
should give a good comparison, or you can specify the type of model to run
`python -m fastrnns.bench --rnns cudnn aten jit --group rnns`
## Run model profiling, calls nvprof
`python -m fastrnns.profile`
should generate nvprof file for all models somewhere.
you can also specify the models to generate nvprof files separately:
`python -m fastrnns.profile --rnns aten jit`
### Caveats
Use Linux for the most accurate timing. A lot of these tests only run
on CUDA.
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