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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>
47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
import torch
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import torch.onnx.symbolic_helper as sym_help
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from torch.onnx import symbolic_opset10, symbolic_opset12
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from torch.onnx.symbolic_helper import parse_args
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@parse_args("v", "v", "v", "v", "i", "none")
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def nll_loss_10(g, self, target, weight=None, reduction="mean", ignore_index=-100):
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if not weight and not ignore_index:
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return g.op("nll_loss", self, target)
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elif ignore_index:
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ignore_index_ = g.op("Constant", value_t=torch.tensor(ignore_index, dtype=torch.int64))
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eq_ = g.op("Equal", target, ignore_index_)
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not_eq_ = g.op("Not", eq_)
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weight_ = g.op("Cast", not_eq_, to_i=1) # FLOAT = 1; // float
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not_eq_int64_ = g.op("Cast", not_eq_, to_i=7) # INT64 = 7; // int64_t
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target_ = g.op("Mul", target, not_eq_int64_)
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# if weight:
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# weight_ = g.op("Mul", weight_, weight)
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return g.op("nll_loss", self, target_, weight_)
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symbolic_opset10.nll_loss = nll_loss_10
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def nll_loss_12(g, self, target, weight, reduction, ignore_index):
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# none reduction : onnx::Constant[value={0}]
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# mean reduction : onnx::Constant[value={1}]
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# sum reduction : onnx::Constant[value={2}]
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reduction = sym_help._maybe_get_const(reduction, "i")
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reduction_vals = ["none", "mean", "sum"]
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reduction = reduction_vals[reduction]
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# in onnx NegativeLogLikelihoodLoss specification, ignore_index is optional without default value.
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# therefore we need to set ignore_index attribute even if it is not specified (e.g. ignore_index=-100).
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ignore_index = sym_help._maybe_get_const(ignore_index, "i")
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if weight.node().mustBeNone():
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nllloss = g.op("NegativeLogLikelihoodLoss", self, target, reduction_s=reduction, ignore_index_i=ignore_index)
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else:
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nllloss = g.op(
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"NegativeLogLikelihoodLoss", self, target, weight, reduction_s=reduction, ignore_index_i=ignore_index
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)
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return nllloss
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symbolic_opset12.nll_loss = nll_loss_12
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