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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>
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
import onnx
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from onnx import TensorProto, helper
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# This is to test the operators without "Qlinear" support but still support uint8 input
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# These operators need to be internal to a graph/partition
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# def GenerateModel(model_name):
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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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"QuantizeLinear",
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["X", "Scale", "Zero_point"],
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["X_quantized"],
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"quantize_0",
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),
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helper.make_node(
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"Concat",
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["X_quantized", "X_quantized"],
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["X_concat"],
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axis=-2,
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name="concat_0",
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),
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helper.make_node(
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"MaxPool",
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["X_concat"],
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["X_maxpool"],
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kernel_shape=[2, 2],
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name="maxpool_0",
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),
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helper.make_node(
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"Transpose",
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["X_maxpool"],
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["X_transposed"],
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perm=[0, 1, 3, 2],
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name="transpose_0",
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),
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helper.make_node(
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"DequantizeLinear",
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["X_transposed", "Scale", "Zero_point"],
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["Y"],
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"dequantize_0",
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),
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]
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initializers = [
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helper.make_tensor("Scale", TensorProto.FLOAT, [1], [256.0]),
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helper.make_tensor("Zero_point", TensorProto.UINT8, [1], [0]),
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]
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inputs = [
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 1, 3]),
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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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inputs,
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[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 1])],
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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_internal_uint8_support.onnx")
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