[IR] Export all common passes in onnxscript.ir.passes.common

[justinchuby]

Summary

Expose all common passes in onnxscript.ir.passes.common. Original apis are retained and unchanged (so not bc breaking).

Rational

In actual usage, exposing the passes under onnxscript.ir.passes.common.[module_name] make the passes harder to be discovered. In pytorch, nn modules are exposed under the same namespace, torch.nn, making it easier for user to find and use them. We use the same idea and expose all common passes under onnxscript.ir.passes.common.

Before this change, users will need to do

from onnxscript.ir.passes.common import unused_removal

unused_removal.RemoveUnusedNodesPass()(model)

With this change, uses can now do

import onnxscript.ir.passes.common as common_passes

common_passes.RemoveUnusedNodesPass()(model)

without having to know the module name.

Updated documentation page

[codecov]

❌ 20 Tests Failed:

Tests completed	Failed	Passed	Skipped
14648	20	14628	1697

View the top 3 failed test(s) by shortest run time

onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_0753_test_or3d

Stack Traces | 0.003s run time

onnxscript\backend\onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
C:\hostedtoolcache\windows\Python\3.11.9\x64\Lib\importlib\__init__.py:126: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
E   ModuleNotFoundError: No module named 'tests.onnx_backend_test_code.test_or3d'

The above exception was the direct cause of the following exception:
.nox\test_ort_nightly\Lib\site-packages\parameterized\parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript\backend\onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript\backend\onnx_export_test.py:139: in extract_functions
    raise AssertionError(
E   AssertionError: Unable to import 'tests.onnx_backend_test_code.test_or3d' (e=No module named 'tests.onnx_backend_test_code.test_or3d') (file: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_or3d.py', absolute path: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_or3d.py', current folder: D:\a\onnxscript\onnxscript
E   ---- CONTENT --
E   import numpy
E   from onnx import TensorProto
E   from onnx.helper import make_tensor
E   from onnxscript import script, external_tensor
E   from onnxscript.values import Opset
E   from onnxscript.onnx_types import BOOL
E   from onnxscript.onnx_opset import opset7
E   
E   @script()
E   def bck_test_or3d(x: BOOL[3,4,5], y: BOOL[3,4,5]) -> (BOOL[3,4,5]):
E       r_or = opset7.Or(x, y)
E       return r_or

onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_1184_test_squeeze_negative_axes

Stack Traces | 0.003s run time

onnxscript\backend\onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
C:\hostedtoolcache\windows\Python\3.11.9\x64\Lib\importlib\__init__.py:126: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
E   ModuleNotFoundError: No module named 'tests.onnx_backend_test_code.test_squeeze_negative_axes'

The above exception was the direct cause of the following exception:
.nox\test_ort_nightly\Lib\site-packages\parameterized\parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript\backend\onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript\backend\onnx_export_test.py:139: in extract_functions
    raise AssertionError(
E   AssertionError: Unable to import 'tests.onnx_backend_test_code.test_squeeze_negative_axes' (e=No module named 'tests.onnx_backend_test_code.test_squeeze_negative_axes') (file: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_squeeze_negative_axes.py', absolute path: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_squeeze_negative_axes.py', current folder: D:\a\onnxscript\onnxscript
E   ---- CONTENT --
E   import numpy
E   from onnx import TensorProto
E   from onnx.helper import make_tensor
E   from onnxscript import script, external_tensor
E   from onnxscript.values import Opset
E   from onnxscript.onnx_types import FLOAT, INT64
E   from onnxscript.onnx_opset import opset21
E   
E   @script()
E   def bck_test_squeeze_negative_axes(x: FLOAT[1,3,1,5], axes: INT64[1]) -> (FLOAT[1,3,5]):
E       y = opset21.Squeeze(x, axes)
E       return y

onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_0926_test_reduce_sum_square_keepdims_random

Stack Traces | 0.004s run time

onnxscript\backend\onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
C:\hostedtoolcache\windows\Python\3.11.9\x64\Lib\importlib\__init__.py:126: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
E   ModuleNotFoundError: No module named 'tests.onnx_backend_test_code.test_reduce_sum_square_keepdims_random'

The above exception was the direct cause of the following exception:
.nox\test_ort_nightly\Lib\site-packages\parameterized\parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript\backend\onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript\backend\onnx_export_test.py:139: in extract_functions
    raise AssertionError(
E   AssertionError: Unable to import 'tests.onnx_backend_test_code.test_reduce_sum_square_keepdims_random' (e=No module named 'tests.onnx_backend_test_code.test_reduce_sum_square_keepdims_random') (file: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_reduce_sum_square_keepdims_random.py', absolute path: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_reduce_sum_square_keepdims_random.py', current folder: D:\a\onnxscript\onnxscript
E   ---- CONTENT --
E   import numpy
E   from onnx import TensorProto
E   from onnx.helper import make_tensor
E   from onnxscript import script, external_tensor
E   from onnxscript.values import Opset
E   from onnxscript.onnx_types import FLOAT, INT64
E   from onnxscript.onnx_opset import opset18
E   
E   @script()
E   def bck_test_reduce_sum_square_keepdims_random(data: FLOAT[3,2,2], axes: INT64[1]) -> (FLOAT[3,1,2]):
E       reduced = opset18.ReduceSumSquare(data, axes, keepdims=1)
E       return reduced

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[Copilot]

Pull Request Overview

This PR exposes all common passes under the common namespace (onnxscript.ir.passes.common) so that users no longer need to import passes from nested modules.

• Updated import statements throughout the codebase to reference the new common namespace.
• Adjusted usage of pass classes in optimizer, version converter, and tests to follow the unified import convention.
• Updated documentation to reflect the new API structure.

Reviewed Changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
tests/function_libs/torch_lib/ops_test_common.py	Changed import to use the new common namespace.
onnxscript/version_converter/init.py	Replaced deep imports with common namespace; introduced an error in an error message string.
onnxscript/optimizer/_optimizer.py	Updated pass usage with new namespace.
onnxscript/optimizer/init.py	Updated pass usage with new namespace.
onnxscript/ir/passes/common/onnx_checker.py	Updated property annotations with Literal types.
onnxscript/ir/passes/common/inliner.py	Added documentation for the InlinePass class.
onnxscript/ir/passes/common/clear_metadata_and_docstring.py	Added docstring for the ClearMetadataAndDocStringPass.
onnxscript/ir/passes/common/init.py	Reorganized exports and removed module-setting logic.
docs/ir/ir_api/ir_passes_common.md	Updated documentation to use automodule.