mirror of https://github.com/llvm/torch-mlir
136 lines
5.5 KiB
Python
136 lines
5.5 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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# Also available under a BSD-style license. See LICENSE.
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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_e2e_test.framework import run_tests
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from torch_mlir_e2e_test.reporting import report_results
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from torch_mlir_e2e_test.registry import GLOBAL_TEST_REGISTRY
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from torch_mlir_e2e_test.serialization import deserialize_all_tests_from
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# Available test configs.
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from torch_mlir_e2e_test.configs import (
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LazyTensorCoreTestConfig,
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LinalgOnTensorsBackendTestConfig,
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MhloBackendTestConfig,
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NativeTorchTestConfig,
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TorchScriptTestConfig,
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TosaBackendTestConfig,
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EagerModeTestConfig
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)
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from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend import RefBackendLinalgOnTensorsBackend
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from torch_mlir_e2e_test.mhlo_backends.linalg_on_tensors import LinalgOnTensorsMhloBackend
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from torch_mlir_e2e_test.tosa_backends.linalg_on_tensors import LinalgOnTensorsTosaBackend
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from .xfail_sets import REFBACKEND_XFAIL_SET, MHLO_PASS_SET, TOSA_PASS_SET, EAGER_MODE_XFAIL_SET, LTC_XFAIL_SET
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# Import tests to register them in the global registry.
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from torch_mlir_e2e_test.test_suite import register_all_tests
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register_all_tests()
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def _get_argparse():
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config_choices = ['native_torch', 'torchscript', 'refbackend', 'mhlo', 'tosa', 'eager_mode', 'lazy_tensor_core']
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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 torch-mlir's RefBackend.
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"mhlo": run through torch-mlir's default MHLO backend.
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"tosa": run through torch-mlir's default TOSA 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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"eager_mode": run through torch-mlir's eager mode frontend, using RefBackend for execution.
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"lazy_tensor_core": run the model through the Lazy Tensor Core frontend and execute the traced graph.
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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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torch-mlir).
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See `build_tools/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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parser.add_argument('-s', '--sequential',
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default=False,
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action='store_true',
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help='run e2e tests sequentially rather than in parallel')
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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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if args.serialized_test_dir:
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deserialize_all_tests_from(args.serialized_test_dir)
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all_test_unique_names = set(
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test.unique_name for test in GLOBAL_TEST_REGISTRY)
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# Find the selected config.
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if args.config == 'refbackend':
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config = LinalgOnTensorsBackendTestConfig(RefBackendLinalgOnTensorsBackend())
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xfail_set = REFBACKEND_XFAIL_SET
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if args.config == 'tosa':
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config = TosaBackendTestConfig(LinalgOnTensorsTosaBackend())
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xfail_set = all_test_unique_names - TOSA_PASS_SET
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if args.config == 'mhlo':
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config = MhloBackendTestConfig(LinalgOnTensorsMhloBackend())
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xfail_set = all_test_unique_names - MHLO_PASS_SET
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elif args.config == 'native_torch':
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config = NativeTorchTestConfig()
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xfail_set = {}
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elif args.config == 'torchscript':
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config = TorchScriptTestConfig()
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xfail_set = {}
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elif args.config == 'eager_mode':
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config = EagerModeTestConfig()
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xfail_set = EAGER_MODE_XFAIL_SET
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elif args.config == 'lazy_tensor_core':
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config = LazyTensorCoreTestConfig()
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xfail_set = LTC_XFAIL_SET
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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, args.sequential, args.verbose)
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# Report the test results.
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failed = report_results(results, xfail_set, args.verbose)
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sys.exit(1 if failed else 0)
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def _suppress_warnings():
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import warnings
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# Ignore warning due to Python bug:
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# https://stackoverflow.com/questions/4964101/pep-3118-warning-when-using-ctypes-array-as-numpy-array
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warnings.filterwarnings("ignore",
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message="A builtin ctypes object gave a PEP3118 format string that does not match its itemsize")
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if __name__ == '__main__':
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_suppress_warnings()
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main()
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