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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>
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
import onnx
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from onnx import TensorProto, helper
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# Since NNAPI EP handles Reshape and Flatten differently,
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# Please see ReshapeOpBuilder::CanSkipReshape in <repo_root>/onnxruntime/core/providers/nnapi/nnapi_builtin/builders/op_builder.cc
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# We have a separated test for these skip reshape scenarios
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def GenerateModel(model_name): # noqa: N802
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nodes = [
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helper.make_node("Flatten", ["X"], ["Flatten_1_Y"], "flatten_1"),
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helper.make_node("MatMul", ["Flatten_1_Y", "MatMul_B"], ["MatMul_Y"], "matmul"),
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helper.make_node("Reshape", ["Y", "Reshape_1_shape"], ["Reshape_1_Y"], "reshape_1"),
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helper.make_node("Gemm", ["Reshape_1_Y", "Gemm_B"], ["Gemm_Y"], "gemm"),
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helper.make_node("Reshape", ["MatMul_Y", "Reshape_2_shape"], ["Reshape_2_Y"], "reshape_2"),
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helper.make_node("Flatten", ["Gemm_Y"], ["Flatten_2_Y"], "flatten_2", axis=0),
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helper.make_node("Add", ["Reshape_2_Y", "Flatten_2_Y"], ["Z"], "add"),
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]
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initializers = [
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helper.make_tensor("Reshape_1_shape", TensorProto.INT64, [2], [3, 4]),
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helper.make_tensor("Reshape_2_shape", TensorProto.INT64, [2], [1, 6]),
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helper.make_tensor(
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"Gemm_B",
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TensorProto.FLOAT,
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[4, 2],
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[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
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),
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helper.make_tensor("MatMul_B", TensorProto.FLOAT, [2, 3], [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]),
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]
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inputs = [
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 1, 2]),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2, 2]),
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]
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graph = helper.make_graph(
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nodes,
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"NNAPI_Reshape_Flatten_Test",
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inputs,
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[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 6])],
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initializers,
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)
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model = helper.make_model(graph)
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onnx.save(model, model_name)
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if __name__ == "__main__":
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GenerateModel("nnapi_reshape_flatten_test.onnx")
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