mirror of https://github.com/llvm/torch-mlir
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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import argparse
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import re
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import sys
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from torch_mlir.torchscript.e2e_test.framework import run_tests
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from torch_mlir.torchscript.e2e_test.reporting import report_results
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from torch_mlir.torchscript.e2e_test.registry import GLOBAL_TEST_REGISTRY
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# Available test configs.
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from torch_mlir.torchscript.e2e_test.configs import (
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RefBackendTestConfig, NativeTorchTestConfig, TorchScriptTestConfig
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)
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# Import tests to register them in the global registry.
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# TODO: Use a relative import.
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# That requires invoking this file as a "package" though, which makes it
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# not possible to just do `python main.py`. Instead, it requires something
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# like `python -m tochscript.main` which is annoying because it can only
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# be run from a specific directory.
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# TODO: Find out best practices for python "main" files.
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import basic
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import vision_models
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import mlp
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import quantized_models
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def _get_argparse():
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parser = argparse.ArgumentParser(description='Run torchscript e2e tests.')
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parser.add_argument('--config',
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choices=['native_torch', 'torchscript', 'refbackend'],
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default='refbackend',
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help='''
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Meaning of options:
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"refbackend": run through npcomp's RefBackend.
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"native_torch": run the torch.nn.Module as-is without compiling (useful for verifying model is deterministic; ALL tests should pass in this configuration).
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"torchscript": compile the model to a torch.jit.ScriptModule, and then run that as-is (useful for verifying TorchScript is modeling the program correctly).
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''')
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parser.add_argument('--filter', default='.*', help='''
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Regular expression specifying which tests to include in this run.
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''')
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parser.add_argument('-v', '--verbose',
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default=False,
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action='store_true',
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help='report test results with additional detail')
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return parser
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def main():
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args = _get_argparse().parse_args()
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# Find the selected config.
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if args.config == 'refbackend':
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config = RefBackendTestConfig()
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elif args.config == 'native_torch':
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config = NativeTorchTestConfig()
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elif args.config == 'torchscript':
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config = TorchScriptTestConfig()
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# Find the selected tests, and emit a diagnostic if none are found.
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tests = [
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test for test in GLOBAL_TEST_REGISTRY
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if re.match(args.filter, test.unique_name)
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]
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if len(tests) == 0:
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print(
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f'ERROR: the provided filter {args.filter!r} does not match any tests'
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)
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print('The available tests are:')
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for test in GLOBAL_TEST_REGISTRY:
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print(test.unique_name)
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sys.exit(1)
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# Run the tests.
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results = run_tests(tests, config)
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# Report the test results.
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report_results(results, args.verbose)
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if __name__ == '__main__':
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main()
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