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Edward Z. Yang dd3a77bc96 Apply UFMT to all files in benchmarks/ (#105928 ) Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/105928 Approved by: https://github.com/albanD		2023-07-26 01:18:48 +00:00
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__init__.py
bench.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
cells.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
conftest.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
custom_lstms.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
factory.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
fuser.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
profile.py	Apply UFMT to all files in benchmarks/ (#105928 )	2023-07-26 01:18:48 +00:00
README.md	Lint trailing newlines (#54737 )	2021-03-30 13:09:52 -07:00
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README.md

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.