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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>
23 lines
722 B
Python
23 lines
722 B
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import sqlite3
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import onnx
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from onnx import numpy_helper
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connection = sqlite3.connect("<path-to-sqldb-from-tracing>", detect_types=sqlite3.PARSE_DECLTYPES)
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def convert_tensor_proto_to_numpy_array(blob):
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tensor_proto = onnx.TensorProto()
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tensor_proto.ParseFromString(blob)
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return numpy_helper.to_array(tensor_proto)
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sqlite3.register_converter("TensorProto", convert_tensor_proto_to_numpy_array)
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for step, name, value, _device, _producer, consumers in connection.execute(
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"Select Step, Name, Value, DeviceType, TracedProducer, TracedConsumers from Tensors"
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):
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print(step, name, value.shape, consumers)
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