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### Description `lintrunner` is a linter runner successfully used by pytorch, onnx and onnx-script. It provides a uniform experience running linters locally and in CI. It supports all major dev systems: Windows, Linux and MacOs. The checks are enforced by the `Python format` workflow. This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors in Python code. `lintrunner` now runs all required python lints including `ruff`(replacing `flake8`), `black` and `isort`. Future lints like `clang-format` can be added. Most errors are auto-fixed by `ruff` and the fixes should be considered robust. Lints that are more complicated to fix are applied `# noqa` for now and should be fixed in follow up PRs. ### Notable changes 1. This PR **removed some suboptimal patterns**: - `not xxx in` -> `xxx not in` membership checks - bare excepts (`except:` -> `except Exception`) - unused imports The follow up PR will remove: - `import *` - mutable values as default in function definitions (`def func(a=[])`) - more unused imports - unused local variables 2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than flake8 and is more robust. We are using it successfully in onnx and onnx-script. It also supports auto-fixing many flake8 errors. 3. Removed the legacy flake8 ci flow and updated docs. 4. The added workflow supports SARIF code scanning reports on github, example snapshot:  5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Unified linting experience in CI and local. Replacing https://github.com/microsoft/onnxruntime/pull/14306 --------- Signed-off-by: Justin Chu <justinchu@microsoft.com>
37 lines
977 B
Python
37 lines
977 B
Python
import onnx
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from onnx import TensorProto, helper
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# Create a model with metadata to test ORT conversion
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def GenerateModel(model_name): # noqa: N802
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nodes = [
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helper.make_node("Sigmoid", ["X"], ["Y"], "sigmoid"),
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]
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graph = helper.make_graph(
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nodes,
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"NNAPI_Internal_uint8_Test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3])],
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[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3])],
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)
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model = helper.make_model(graph)
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# Add meta data
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model.doc_string = "This is doc_string"
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model.producer_name = "TensorTorch"
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model.model_version = 12345
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model.domain = "ai.onnx.ml"
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helper.set_model_props(
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model,
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{
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"I am key 1!": "I am value 1!",
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"": "Value for empty key!",
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"Key for empty value!": "",
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},
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)
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onnx.save(model, model_name)
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if __name__ == "__main__":
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GenerateModel("model_with_metadata.onnx")
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