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Xuehai Pan a229b4526f [BE] Prefer dash over underscore in command-line options (#94505) 
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.

Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:

`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)

```python
class BooleanOptionalAction(Action):
    def __init__(...):
            if option_string.startswith('--'):
                option_string = '--no-' + option_string[2:]
                _option_strings.append(option_string)
```

It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
2023-02-09 20:16:49 +00:00
..
__init__.py
bench.py [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
cells.py
conftest.py
custom_lstms.py
factory.py
fuser.py
profile.py [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
README.md
runner.py
scratch.py
test.py [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
test_bench.py

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.