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a229b4526f
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
168 lines
4.6 KiB
Python
168 lines
4.6 KiB
Python
import argparse
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import torch
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import benchmark_core
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import benchmark_utils
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"""Performance microbenchmarks's main binary.
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This is the main function for running performance microbenchmark tests.
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It also registers existing benchmark tests via Python module imports.
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"""
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parser = argparse.ArgumentParser(
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description="Run microbenchmarks.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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def parse_args():
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parser.add_argument(
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'--tag-filter',
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'--tag_filter',
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help='tag_filter can be used to run the shapes which matches the tag. (all is used to run all the shapes)',
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default='short')
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# This option is used to filter test cases to run.
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parser.add_argument(
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'--operators',
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help='Filter tests based on comma-delimited list of operators to test',
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default=None)
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parser.add_argument(
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'--operator-range',
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'--operator_range',
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help='Filter tests based on operator_range(e.g. a-c or b,c-d)',
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default=None)
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parser.add_argument(
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'--test-name',
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'--test_name',
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help='Run tests that have the provided test_name',
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default=None)
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parser.add_argument(
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'--list-ops',
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'--list_ops',
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help='List operators without running them',
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action='store_true')
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parser.add_argument(
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'--list-tests',
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'--list_tests',
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help='List all test cases without running them',
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action='store_true')
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parser.add_argument(
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"--iterations",
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help="Repeat each operator for the number of iterations",
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type=int
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)
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parser.add_argument(
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"--num-runs",
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"--num_runs",
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help="Run each test for num_runs. Each run executes an operator for number of <--iterations>",
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type=int,
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default=1,
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)
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parser.add_argument(
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"--min-time-per-test",
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"--min_time_per_test",
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help="Set the minimum time (unit: seconds) to run each test",
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type=int,
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default=0,
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)
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parser.add_argument(
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"--warmup-iterations",
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"--warmup_iterations",
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help="Number of iterations to ignore before measuring performance",
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default=100,
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type=int
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)
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parser.add_argument(
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"--omp-num-threads",
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"--omp_num_threads",
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help="Number of OpenMP threads used in PyTorch/Caffe2 runtime",
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default=None,
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type=int
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)
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parser.add_argument(
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"--mkl-num-threads",
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"--mkl_num_threads",
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help="Number of MKL threads used in PyTorch/Caffe2 runtime",
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default=None,
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type=int
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)
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parser.add_argument(
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"--report-aibench",
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"--report_aibench",
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type=benchmark_utils.str2bool,
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nargs='?',
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const=True,
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default=False,
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help="Print result when running on AIBench"
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)
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parser.add_argument(
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"--use-jit",
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"--use_jit",
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type=benchmark_utils.str2bool,
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nargs='?',
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const=True,
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default=False,
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help="Run operators with PyTorch JIT mode"
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)
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parser.add_argument(
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"--forward-only",
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"--forward_only",
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type=benchmark_utils.str2bool,
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nargs='?',
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const=True,
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default=False,
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help="Only run the forward path of operators"
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)
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parser.add_argument(
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'--framework',
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help='Comma-delimited list of frameworks to test (Caffe2, PyTorch)',
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default="Caffe2,PyTorch")
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parser.add_argument(
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'--device',
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help='Run tests on the provided architecture (cpu, cuda)',
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default='None')
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args, _ = parser.parse_known_args()
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if args.omp_num_threads:
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# benchmark_utils.set_omp_threads sets the env variable OMP_NUM_THREADS
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# which doesn't have any impact as C2 init logic has already been called
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# before setting the env var.
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# In general, OMP_NUM_THREADS (and other OMP env variables) needs to be set
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# before the program is started.
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# From Chapter 4 in OMP standard: https://www.openmp.org/wp-content/uploads/openmp-4.5.pdf
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# "Modifications to the environment variables after the program has started,
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# even if modified by the program itself, are ignored by the OpenMP implementation"
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benchmark_utils.set_omp_threads(args.omp_num_threads)
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if benchmark_utils.is_pytorch_enabled(args.framework):
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torch.set_num_threads(args.omp_num_threads)
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if args.mkl_num_threads:
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benchmark_utils.set_mkl_threads(args.mkl_num_threads)
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return args
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def main():
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args = parse_args()
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benchmark_core.BenchmarkRunner(args).run()
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if __name__ == "__main__":
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main()
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