torch-mlir/frontends/pytorch/e2e_testing/torchscript/main.py

#  Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
#  See https://llvm.org/LICENSE.txt for license information.
#  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception

import argparse
import re
import sys

from torch_mlir.torchscript.e2e_test.framework import run_tests
from torch_mlir.torchscript.e2e_test.reporting import report_results
from torch_mlir.torchscript.e2e_test.registry import GLOBAL_TEST_REGISTRY

# Available test configs.
from torch_mlir.torchscript.e2e_test.configs import (
    RefBackendTestConfig, NativeTorchTestConfig, TorchScriptTestConfig
)

# Import tests to register them in the global registry.
# TODO: Use a relative import.
# That requires invoking this file as a "package" though, which makes it
# not possible to just do `python main.py`. Instead, it requires something
# like `python -m tochscript.main` which is annoying because it can only
# be run from a specific directory.
# TODO: Find out best practices for python "main" files.
import basic
import vision_models
import mlp
import quantized_models

def _get_argparse():
    parser = argparse.ArgumentParser(description='Run torchscript e2e tests.')
    parser.add_argument('--config',
        choices=['native_torch', 'torchscript', 'refbackend'],
        default='refbackend',
        help='''
Meaning of options:
"refbackend": run through npcomp's RefBackend.
"native_torch": run the torch.nn.Module as-is without compiling (useful for verifying model is deterministic; ALL tests should pass in this configuration).
"torchscript": compile the model to a torch.jit.ScriptModule, and then run that as-is (useful for verifying TorchScript is modeling the program correctly).
''')
    parser.add_argument('--filter', default='.*', help='''
Regular expression specifying which tests to include in this run.
''')
    parser.add_argument('-v', '--verbose',
                        default=False,
                        action='store_true',
                        help='report test results with additional detail')
    return parser

def main():
    args = _get_argparse().parse_args()

    # Find the selected config.
    if args.config == 'refbackend':
        config = RefBackendTestConfig()
    elif args.config == 'native_torch':
        config = NativeTorchTestConfig()
    elif args.config == 'torchscript':
        config = TorchScriptTestConfig()

    # Find the selected tests, and emit a diagnostic if none are found.
    tests = [
        test for test in GLOBAL_TEST_REGISTRY
        if re.match(args.filter, test.unique_name)
    ]
    if len(tests) == 0:
        print(
            f'ERROR: the provided filter {args.filter!r} does not match any tests'
        )
        print('The available tests are:')
        for test in GLOBAL_TEST_REGISTRY:
            print(test.unique_name)
        sys.exit(1)

    # Run the tests.
    results = run_tests(tests, config)

    # Report the test results.
    report_results(results, args.verbose)

if __name__ == '__main__':
    main()