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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>
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
import onnx
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from onnx import TensorProto, helper
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# Since NNAPI EP does not support dynamic shape input and we now switch from the approach of immediately rejecting
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# the whole graph in NNAPI EP if it has a dynamic input to checking the dynamic shape at individual operator support check level,
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# We have a separated test here using a graph with dynamic input that becomes fixed after a Resize
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# Please see BaseOpBuilder::HasSupportedInputs in <repo_root>/onnxruntime/core/providers/nnapi/nnapi_builtin/builders/op_support_checker.cc
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def GenerateModel(model_name): # noqa: N802
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nodes = [
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helper.make_node(
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"Resize",
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["X", "", "", "Resize_1_sizes"],
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["Resize_1_output"],
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"resize_1",
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mode="cubic",
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),
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helper.make_node("Add", ["Resize_1_output", "Add_2_input"], ["Y"], "add"),
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]
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initializers = [
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helper.make_tensor("Resize_1_sizes", TensorProto.INT64, [4], [1, 1, 3, 3]),
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helper.make_tensor(
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"Add_2_input",
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TensorProto.FLOAT,
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[1, 1, 3, 3],
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[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
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),
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]
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inputs = [
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helper.make_tensor_value_info("X", TensorProto.FLOAT, ["1", "1", "N", "N"]), # used dim_param here
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]
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outputs = [
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3]),
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]
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graph = helper.make_graph(nodes, "EP_Dynamic_Graph_Input_Test", inputs, outputs, initializers)
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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("ep_dynamic_graph_input_test.onnx")
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