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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>
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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.. _l-example-profiling:
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Profile the execution of a simple model
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=======================================
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*ONNX Runtime* can profile the execution of the model.
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This example shows how to interpret the results.
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"""
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import numpy
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import onnx
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import onnxruntime as rt
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from onnxruntime.datasets import get_example
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def change_ir_version(filename, ir_version=6):
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"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"
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with open(filename, "rb") as f:
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model = onnx.load(f)
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model.ir_version = 6
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if model.opset_import[0].version <= 7:
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model.opset_import[0].version = 11
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return model
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#########################
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# Let's load a very simple model and compute some prediction.
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example1 = get_example("mul_1.onnx")
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onnx_model = change_ir_version(example1)
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onnx_model_str = onnx_model.SerializeToString()
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sess = rt.InferenceSession(onnx_model_str, providers=rt.get_available_providers())
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input_name = sess.get_inputs()[0].name
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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res = sess.run(None, {input_name: x})
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print(res)
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#########################
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# We need to enable to profiling
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# before running the predictions.
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options = rt.SessionOptions()
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options.enable_profiling = True
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sess_profile = rt.InferenceSession(onnx_model_str, options, providers=rt.get_available_providers())
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input_name = sess.get_inputs()[0].name
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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sess.run(None, {input_name: x})
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prof_file = sess_profile.end_profiling()
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print(prof_file)
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###########################
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# The results are stored un a file in JSON format.
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# Let's see what it contains.
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import json # noqa: E402
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with open(prof_file) as f:
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sess_time = json.load(f)
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import pprint # noqa: E402
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pprint.pprint(sess_time)
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