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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>
34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
import numpy as np # noqa: F401
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import onnx
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from onnx import GraphProto, OperatorSetIdProto, TensorProto, helper, numpy_helper # noqa: F401
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X1 = helper.make_tensor_value_info("x1", TensorProto.INT64, [4, 4])
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X2 = helper.make_tensor_value_info("x2", TensorProto.INT64, [4, 4])
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Y1 = helper.make_tensor_value_info("output1", TensorProto.INT64, [4, 4])
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Y2 = helper.make_tensor_value_info("output2", TensorProto.INT64, [4, 4])
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add1 = helper.make_node("Add", ["x1", "x2"], ["add1"], name="add1")
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add2 = helper.make_node("Add", ["x1", "x2"], ["add2"], name="add2")
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id1 = helper.make_node("Identity", ["add1"], ["output1"], name="id1")
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id2 = helper.make_node("Identity", ["add2"], ["output2"], name="id2")
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# Create the graph (GraphProto)
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graph_def = helper.make_graph([add1, add2, id1, id2], "identity_elimination_model", [X1, X2], [Y1, Y2])
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opsets = []
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onnxdomain = OperatorSetIdProto()
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onnxdomain.version = 12
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onnxdomain.domain = "" # The empty string ("") or absence of this field implies the operator set that is defined as part of the ONNX specification.
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opsets.append(onnxdomain)
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msdomain = OperatorSetIdProto()
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msdomain.version = 1
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msdomain.domain = "com.microsoft"
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opsets.append(msdomain)
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kwargs = {}
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kwargs["opset_imports"] = opsets
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# Create the model (ModelProto)
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model_def = helper.make_model(graph_def, producer_name="onnx-example", **kwargs)
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onnx.save(model_def, "id-elim.onnx")
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