| # 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. |
