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[BE][Easy][19/19] enforce style for empty lines in import segments in torch/[o-z]*/ (#129771)
See #129751 (comment). Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: #129771 Approved by: https://github.com/justinchuby, https://github.com/janeyx99
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‎tools/linter/adapters/ufmt_linter.py

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# torch/[e-n]*/**
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"torch/[e-n]*/**",
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# torch/[o-z]*/**
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"torch/[o-z]*/**",
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],
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),
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)

‎torch/onnx/__init__.py

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from torch._C import _onnx as _C_onnx
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from torch._C._onnx import OperatorExportTypes, TensorProtoDataType, TrainingMode
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from . import ( # usort:skip. Keep the order instead of sorting lexicographically
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from ._exporter_states import ExportTypes
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from ._internal.onnxruntime import (
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is_onnxrt_backend_supported,
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OrtBackend as _OrtBackend,
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OrtBackendOptions as _OrtBackendOptions,
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OrtExecutionProvider as _OrtExecutionProvider,
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)
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from ._type_utils import JitScalarType
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from .errors import CheckerError # Backwards compatibility
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from .utils import (
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_optimize_graph,
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_run_symbolic_function,
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_run_symbolic_method,
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export,
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export_to_pretty_string,
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is_in_onnx_export,
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register_custom_op_symbolic,
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select_model_mode_for_export,
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unregister_custom_op_symbolic,
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)
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from . import ( # usort: skip. Keep the order instead of sorting lexicographically
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_deprecation,
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errors,
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symbolic_caffe2,
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utils,
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)
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from ._exporter_states import ExportTypes
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from ._type_utils import JitScalarType
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from .errors import CheckerError # Backwards compatibility
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from .utils import (
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_optimize_graph,
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_run_symbolic_function,
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_run_symbolic_method,
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export,
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export_to_pretty_string,
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is_in_onnx_export,
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register_custom_op_symbolic,
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select_model_mode_for_export,
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unregister_custom_op_symbolic,
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)
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from ._internal.exporter import ( # usort:skip. needs to be last to avoid circular import
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from ._internal.exporter import ( # usort: skip. needs to be last to avoid circular import
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DiagnosticOptions,
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ExportOptions,
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ONNXProgram,
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enable_fake_mode,
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)
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from ._internal.onnxruntime import (
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is_onnxrt_backend_supported,
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OrtBackend as _OrtBackend,
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OrtBackendOptions as _OrtBackendOptions,
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OrtExecutionProvider as _OrtExecutionProvider,
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)
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__all__ = [
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# Modules

‎torch/onnx/_deprecation.py

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from typing import Callable, TypeVar
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from typing_extensions import ParamSpec
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_T = TypeVar("_T")
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_P = ParamSpec("_P")
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‎torch/onnx/_internal/diagnostics/__init__.py

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from ._rules import rules
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from .infra import levels
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__all__ = [
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"TorchScriptOnnxExportDiagnostic",
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"ExportDiagnosticEngine",

‎torch/onnx/_internal/diagnostics/_diagnostic.py

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from typing import TYPE_CHECKING
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import torch
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from torch.onnx._internal.diagnostics import infra
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from torch.onnx._internal.diagnostics.infra import formatter, sarif
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from torch.onnx._internal.diagnostics.infra.sarif import version as sarif_version
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from torch.utils import cpp_backtrace
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if TYPE_CHECKING:
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from collections.abc import Generator
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‎torch/onnx/_internal/diagnostics/_rules.py

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# flake8: noqa
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from torch.onnx._internal.diagnostics import infra
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< 10000 /td>
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"""
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GENERATED CODE - DO NOT EDIT DIRECTLY
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The purpose of generating a class for each rule is to override the `format_message`

‎torch/onnx/_internal/diagnostics/infra/__init__.py

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)
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from .context import Diagnostic, DiagnosticContext, RuntimeErrorWithDiagnostic
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__all__ = [
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"Diagnostic",
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"DiagnosticContext",

‎torch/onnx/_internal/diagnostics/infra/context.py

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from __future__ import annotations
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import contextlib
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import dataclasses
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import gzip
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import logging
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from typing import Callable, Generator, Generic, Literal, Mapping, TypeVar
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from typing_extensions import Self
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from torch.onnx._internal.diagnostics import infra

‎torch/onnx/_internal/diagnostics/infra/formatter.py

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from typing import Any, Callable, Union
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from torch._logging import LazyString
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from torch.onnx._internal.diagnostics.infra import sarif
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‎torch/onnx/_internal/diagnostics/infra/sarif/__init__.py

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from torch.onnx._internal.diagnostics.infra.sarif._web_request import WebRequest
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from torch.onnx._internal.diagnostics.infra.sarif._web_response import WebResponse
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# flake8: noqa

‎torch/onnx/_internal/diagnostics/infra/sarif/version.py

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from typing import Final
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SARIF_VERSION: Final = "2.1.0"
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SARIF_SCHEMA_LINK: Final = "https://docs.oasis-open.org/sarif/sarif/v2.1.0/cs01/schemas/sarif-schema-2.1.0.json"
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# flake8: noqa

‎torch/onnx/_internal/diagnostics/infra/utils.py

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from __future__ import annotations
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import functools
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import inspect
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import traceback
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from typing import Any, Callable, Mapping, Sequence

‎torch/onnx/_internal/exporter.py

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)
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import abc
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import contextlib
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import dataclasses
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import logging
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import os
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import tempfile
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import warnings
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from collections import defaultdict
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from typing_extensions import Self
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import torch
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import torch._ops
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import torch.export as torch_export
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import torch.utils._pytree as pytree
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from torch.onnx._internal import io_adapter
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from torch.onnx._internal.diagnostics import infra
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from torch.onnx._internal.fx import (
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serialization as fx_serialization,
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)
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# We can only import onnx from this module in a type-checking context to ensure that
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# 'import torch.onnx' continues to work without having 'onnx' installed. We fully
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# 'import onnx' inside of dynamo_export (by way of _assert_dependencies).
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if TYPE_CHECKING:
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import io
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import onnx
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import onnxruntime # type: ignore[import]
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import onnxscript # type: ignore[import]
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from onnxscript.function_libs.torch_lib import ( # type: ignore[import]
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registration as torchlib_registry,
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)
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from torch._subclasses import fake_tensor
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from torch.onnx._internal.fx import diagnostics
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_DEFAULT_OPSET_VERSION: Final[int] = 18

‎torch/onnx/_internal/fx/_pass.py

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from __future__ import annotations
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import abc
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import contextlib
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import dataclasses
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import difflib
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import io
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import logging
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import sys
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from typing import Any, Callable, TYPE_CHECKING
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import torch
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import torch.fx
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from torch.fx.experimental.proxy_tensor import maybe_disable_fake_tensor_mode
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from torch.onnx._internal.fx import diagnostics, onnxfunction_dispatcher
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if TYPE_CHECKING:
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from torch._subclasses import fake_tensor
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‎torch/onnx/_internal/fx/analysis/__init__.py

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from .unsupported_nodes import UnsupportedFxNodesAnalysis
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__all__ = [
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"UnsupportedFxNodesAnalysis",
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]

‎torch/onnx/_internal/fx/decomposition_skip.py

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import abc
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import contextlib
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from typing import Callable, Sequence
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from onnxscript.function_libs.torch_lib.ops import ( # type: ignore[import-not-found]
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import torch
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from torch._decomp import decompositions
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_NEW_OP_NAMESPACE: str = "onnx_export"
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"""The namespace for the custom operator."""
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‎torch/onnx/_internal/fx/decomposition_table.py

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import torch
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import torch._ops
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import torch.fx
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from torch.onnx._internal.fx import registration
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‎torch/onnx/_internal/fx/diagnostics.py

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from __future__ import annotations
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import dataclasses
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import functools
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from typing import Any, TYPE_CHECKING
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import onnxscript # type: ignore[import]
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from torch.onnx._internal.diagnostics.infra import decorator, formatter
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from torch.onnx._internal.fx import registration, type_utils as fx_type_utils
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if TYPE_CHECKING:
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import logging
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‎torch/onnx/_internal/fx/fx_onnx_interpreter.py

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import torch
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import torch.fx
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from torch.onnx import _type_utils as jit_type_utils
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from torch.onnx._internal.fx import (
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_pass,
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diagnostics,

‎torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py

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from __future__ import annotations
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import functools
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from typing import Any, Callable, Mapping, Sequence
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import torch
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import torch.fx
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import torch.onnx
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import torch.onnx._internal.fx.passes as passes
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from torch.onnx._internal import exporter, io_adapter
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# Functions directly wrapped to produce torch.fx.Proxy so that symbolic
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# data can flow through those functions. Python functions (e.g., `torch.arange`)
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# not defined by pybind11 in C++ do not go though Python dispatcher, so

‎torch/onnx/_internal/fx/onnxfunction_dispatcher.py

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import torch
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import torch._ops
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import torch.fx
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from torch.onnx._internal.fx import (
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diagnostics,
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registration,
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type_utils as fx_type_utils,
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)
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if TYPE_CHECKING:
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import onnxscript # type: ignore[import]
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from onnxscript.function_libs.torch_lib import ( # type: ignore[import]

‎torch/onnx/_internal/fx/op_validation.py

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import torch.fx
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from torch.fx.experimental import symbolic_shapes
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from torch.onnx import _constants, _type_utils as jit_type_utils
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from torch.onnx._internal.fx import (
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diagnostics,
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fx_onnx_interpreter,

‎torch/onnx/_internal/fx/passes/__init__.py

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from .type_promotion import InsertTypePromotion
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from .virtualization import MovePlaceholderToFront, ReplaceGetAttrWithPlaceholder
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__all__ = [
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"Decompose",
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"InsertTypePromotion",

‎torch/onnx/_internal/fx/passes/_utils.py

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from __future__ import annotations
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import collections
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import re
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from typing import Callable
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import torch.fx

‎tor 10000 ch/onnx/_internal/fx/passes/decomp.py

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from __future__ import annotations
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import contextlib
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from typing import Callable, Mapping, TYPE_CHECKING
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import torch
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from torch.onnx._internal.fx import _pass, diagnostics
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from torch.onnx._internal.fx.passes import _utils
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if TYPE_CHECKING:
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import torch.fx
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from torch._subclasses import fake_tensor

‎torch/onnx/_internal/fx/passes/functionalization.py

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from __future__ import annotations
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import contextlib
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from typing import Callable, TYPE_CHECKING
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import torch
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from torch.onnx._internal.fx.passes import _utils
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from torch.utils import _pytree as pytree
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if TYPE_CHECKING:
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from torch._subclasses import fake_tensor
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