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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>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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"""
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Implements ONNX's backend API.
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"""
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from typing import Any, Tuple # noqa: F401
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from onnx.backend.base import BackendRep
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from onnxruntime import RunOptions
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class OnnxRuntimeBackendRep(BackendRep):
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"""
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Computes the prediction for a pipeline converted into
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an :class:`onnxruntime.InferenceSession` node.
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"""
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def __init__(self, session):
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"""
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:param session: :class:`onnxruntime.InferenceSession`
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"""
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self._session = session
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def run(self, inputs, **kwargs): # type: (Any, **Any) -> Tuple[Any, ...]
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"""
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Computes the prediction.
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See :meth:`onnxruntime.InferenceSession.run`.
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"""
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options = RunOptions()
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for k, v in kwargs.items():
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if hasattr(options, k):
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setattr(options, k, v)
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if isinstance(inputs, list):
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inps = {}
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for i, inp in enumerate(self._session.get_inputs()):
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inps[inp.name] = inputs[i]
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outs = self._session.run(None, inps, options)
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if isinstance(outs, list):
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return outs
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else:
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output_names = [o.name for o in self._session.get_outputs()]
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return [outs[name] for name in output_names]
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else:
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inp = self._session.get_inputs()
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if len(inp) != 1:
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raise RuntimeError(f"Model expect {len(inp)} inputs")
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inps = {inp[0].name: inputs}
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return self._session.run(None, inps, options)
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