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Add framework-agnostic tests for common components (#1575)
Signed-off-by: yiliu30 <[email protected]> Co-authored-by: chensuyue <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| """Tests for common components. | ||
| !!! Please do not import any framework-specific modules in this file. !!! | ||
| * Note, we may need to add some auto check mechanisms to ensure this. | ||
| These tests aim to assess the fundamental functionalities of common components and enhance code coverage. | ||
| All tests will be included for each framework CI. | ||
| * Note | ||
| The folder structure: | ||
| . | ||
| ├── 3x | ||
| │ ├── common | ||
| │ ├── onnxrt | ||
| │ ├── tensorflow | ||
| │ └── torch | ||
| For each fwk CI: | ||
| onnxrt_included_folder: | ||
| ├── 3x | ||
| │ ├── common | ||
| │ ├── onnxrt | ||
| tensorflow_included_folder: | ||
| ├── 3x | ||
| │ ├── common | ||
| │ ├── tensorflow | ||
| torch_included_folder: | ||
| ├── 3x | ||
| │ ├── common | ||
| │ ├── torch | ||
| """ | ||
|
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| import unittest | ||
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| from neural_compressor.common import Logger | ||
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| logger = Logger().get_logger() | ||
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| from typing import Any, Callable, List, Optional, Tuple, Union | ||
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| from neural_compressor.common.base_config import BaseConfig, get_all_config_set_from_config_registry, register_config | ||
| from neural_compressor.common.utils import DEFAULT_WHITE_LIST, OP_NAME_OR_MODULE_TYPE | ||
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| PRIORITY_FAKE_ALGO = 100 | ||
| FAKE_CONFIG_NAME = "fake" | ||
| DEFAULT_WEIGHT_BITS = [4, 6] | ||
|
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| FAKE_FRAMEWORK_NAME = "FAKE_FWK" | ||
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| @register_config(framework_name=FAKE_FRAMEWORK_NAME, algo_name=FAKE_CONFIG_NAME, priority=PRIORITY_FAKE_ALGO) | ||
| class FakeAlgoConfig(BaseConfig): | ||
| """Config class for fake algo.""" | ||
|
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| supported_configs: List = [] | ||
| params_list = [ | ||
| "weight_dtype", | ||
| "weight_bits", | ||
| ] | ||
| name = FAKE_CONFIG_NAME | ||
|
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| def __init__( | ||
| self, | ||
| weight_dtype: str = "int", | ||
| weight_bits: int = 4, | ||
| white_list: Optional[List[OP_NAME_OR_MODULE_TYPE]] = DEFAULT_WHITE_LIST, | ||
| ): | ||
| """Init fake config. | ||
| Args: | ||
| weight_dtype (str): Data type for weights, default is "int". | ||
| weight_bits (int): Number of bits used to represent weights, default is 4. | ||
| """ | ||
| super().__init__(white_list=white_list) | ||
| self.weight_bits = weight_bits | ||
| self.weight_dtype = weight_dtype | ||
| self._post_init() | ||
|
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| def to_dict(self): | ||
| return super().to_dict() | ||
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| @classmethod | ||
| def from_dict(cls, config_dict): | ||
| return super(FakeAlgoConfig, cls).from_dict(config_dict=config_dict) | ||
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| @classmethod | ||
| def register_supported_configs(cls) -> List: | ||
| pass | ||
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| @staticmethod | ||
| def get_model_info(model: Any) -> List[Tuple[str, Callable]]: | ||
| pass | ||
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| @classmethod | ||
| def get_config_set_for_tuning(cls) -> Union[None, "FakeAlgoConfig", List["FakeAlgoConfig"]]: | ||
| return FakeAlgoConfig(weight_bits=DEFAULT_WEIGHT_BITS) | ||
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| FakeAlgoConfig.register_supported_configs() | ||
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| def get_default_fake_config() -> FakeAlgoConfig: | ||
| """Generate the default fake config. | ||
| Returns: | ||
| the default fake config. | ||
| """ | ||
| return FakeAlgoConfig() | ||
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| def get_all_config_set() -> Union[BaseConfig, List[BaseConfig]]: | ||
| return get_all_config_set_from_config_registry(fwk_name=FAKE_FRAMEWORK_NAME) | ||
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| class TestBaseConfig(unittest.TestCase): | ||
| @classmethod | ||
| def setUpClass(self): | ||
| pass | ||
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| @classmethod | ||
| def tearDownClass(self): | ||
| pass | ||
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| def setUp(self): | ||
| # print the test name | ||
| logger.info(f"Running TestBaseConfig test: {self.id()}") | ||
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| def test_api(self): | ||
| fake_default_config = get_default_fake_config() | ||
| self.assertEqual(fake_default_config.weight_dtype, "int") | ||
| config_set = get_all_config_set() | ||
| self.assertEqual(len(config_set), 1) | ||
| self.assertEqual(config_set[0].weight_bits, DEFAULT_WEIGHT_BITS) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |