f59581218f
Summary: alloctor -> allocator excutable -> executable excution -> execution foward -> forward initiaize -> initialize paralell -> parallel preprocesor -> preprocessor tranpose -> transpose Pull Request resolved: https://github.com/pytorch/pytorch/pull/21665 Differential Revision: D15806155 Pulled By: soumith fbshipit-source-id: d92b21ec8650a2b32f05faf9af0b7d2b073e992c |
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| .. | ||
| __init__.py | ||
| bench.py | ||
| cells.py | ||
| custom_lstms.py | ||
| factory.py | ||
| profile.py | ||
| README.md | ||
| runner.py | ||
| scratch.py | ||
| test.py | ||
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 shieldwhen 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 comparision, 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.