mirror of https://github.com/llvm/torch-mlir
116 lines
4.2 KiB
Python
116 lines
4.2 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 os
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import pickle
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import re
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import sys
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from npcomp_torchscript.e2e_test.framework import run_tests
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from npcomp_torchscript.e2e_test.reporting import report_results
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from npcomp_torchscript.e2e_test.registry import GLOBAL_TEST_REGISTRY
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# Available test configs.
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from npcomp_torchscript_e2e_test_configs import (
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NpcompBackendTestConfig, NativeTorchTestConfig, TorchScriptTestConfig
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)
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from npcomp.compiler.pytorch.backend import is_iree_enabled
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IREE_ENABLED = is_iree_enabled()
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if IREE_ENABLED:
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from npcomp.compiler.pytorch.backend.iree import IreeNpcompBackend
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from npcomp.compiler.pytorch.backend.refjit import RefjitNpcompBackend
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from .xfail_sets import XFAIL_SETS
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# Import tests to register them in the global registry.
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# Make sure to use `tools/torchscript_e2e_test.sh` wrapper for invoking
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# this script.
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from . import basic
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from . import vision_models
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from . import mlp
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from . import conv
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from . import batchnorm
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from . import quantized_models
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from . import elementwise
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from . import list_programs
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from . import reduction
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def _get_argparse():
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config_choices = ['native_torch', 'torchscript', 'refbackend']
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if IREE_ENABLED:
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config_choices += ['iree']
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parser = argparse.ArgumentParser(description='Run torchscript e2e tests.')
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parser.add_argument('-c', '--config',
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choices=config_choices,
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default='refbackend',
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help=f'''
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Meaning of options:
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"refbackend": run through npcomp's RefBackend.
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"iree"{'' if IREE_ENABLED else '(disabled)'}: run through npcomp's IREE backend.
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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('-f', '--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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parser.add_argument('--serialized-test-dir', default=None, type=str, help='''
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The directory containing serialized pre-built tests.
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Right now, these are additional tests which require heavy Python dependencies
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to generate (or cannot even be generated with the version of PyTorch used by
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npcomp).
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See `build_tools/torchscript_e2e_heavydep_tests/generate_serialized_tests.sh`
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for more information on building these artifacts.
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''')
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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 = NpcompBackendTestConfig(RefjitNpcompBackend())
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elif args.config == 'iree':
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config = NpcompBackendTestConfig(IreeNpcompBackend())
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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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all_tests = list(GLOBAL_TEST_REGISTRY)
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if args.serialized_test_dir:
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for root, dirs, files in os.walk(args.serialized_test_dir):
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for filename in files:
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with open(os.path.join(root, filename), 'rb') as f:
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all_tests.append(pickle.load(f).as_test())
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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 all_tests
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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 all_tests:
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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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failed = report_results(results, XFAIL_SETS[args.config], args.verbose)
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sys.exit(1 if failed else 0)
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if __name__ == '__main__':
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main()
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