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[Stable APIs] Create torchlib_opset for torch 2.6 #1963

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merged 3 commits into from
Nov 23, 2024
Merged

[Stable APIs] Create torchlib_opset for torch 2.6 #1963

merged 3 commits into from
Nov 23, 2024

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justinchuby
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Create torchlib_opset for torch 2.6. This will be used for creating the model opset import as well as in _building for creating constant/concat nodes etc.

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codecov bot commented Nov 22, 2024 •
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❌ 16 Tests Failed:

Tests completed Failed Passed Skipped
14307 16 14291 1623
View the full list of 3 ❄️ flaky tests 
tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime::test_function_all_input_by_kwargs

Flake rate in main: 39.73% (Passed 9442 times, Failed 6223 times)

Stack Traces | 0.002s run time 
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:109: in test_function_all_input_by_kwargs
    self.assertEqual(add_with_alpha(this=1.0, other=2.0), 3.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').
tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime::test_function_some_input_by_kwargs

Flake rate in main: 39.73% (Passed 9442 times, Failed 6223 times)

Stack Traces | 0.003s run time 
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:106: in test_function_some_input_by_kwargs
    self.assertEqual(add_with_alpha(1.0, other=2.0), 3.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').
tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime::test_function_attribute_by_positional_args

Flake rate in main: 39.73% (Passed 9442 times, Failed 6223 times)

Stack Traces | 0.003s run time 
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:112: in test_function_attribute_by_positional_args
    self.assertEqual(add_with_alpha(1.0, 2.0, 3.0), 7.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').

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@justinchuby justinchuby mentioned this pull request Nov 23, 2024
Closed
@justinchuby justinchuby merged commit 9592227 into main Nov 23, 2024
22 of 39 checks passed
@justinchuby justinchuby deleted the justinchu/api-opset branch November 23, 2024 03:03
pytorchmergebot pushed a commit to pytorch/pytorch that referenced this pull request Nov 25, 2024
@justinchuby @pytorchmergebot
[ONNX] Use the torchlib opset number and fix opset import logic (#141413
419b566 
)

- Update the ONNX IR `add_opset_imports` pass to remove the heuristics of taking the `max` of the seen opsets. Instead, it uses the torchlib default opset version for the model's opset_import. The version converter is able to take the true opset versions in the nodes and convert the model to the correct version.
- Update all hard coding of opset 18 to instead query the default torchlib opset from onnxscript, introduced in microsoft/onnxscript#1963

Fixes #141260
Pull Request resolved: #141413
Approved by: https://github.com/titaiwangms
Ryo-not-rio pushed a commit to Ryo-not-rio/pytorch that referenced this pull request Dec 2, 2024
@justinchuby @Ryo-not-rio
[ONNX] Use the torchlib opset number and fix opset import logic (pyto…
5e4c7dc 
…rch#141413)

- Update the ONNX IR `add_opset_imports` pass to remove the heuristics of taking the `max` of the seen opsets. Instead, it uses the torchlib default opset version for the model's opset_import. The version converter is able to take the true opset versions in the nodes and convert the model to the correct version.
- Update all hard coding of opset 18 to instead query the default torchlib opset from onnxscript, introduced in microsoft/onnxscript#1963

Fixes pytorch#141260
Pull Request resolved: pytorch#141413
Approved by: https://github.com/titaiwangms
pobin6 pushed a commit to pobin6/pytorch that referenced this pull request Dec 5, 2024
@justinchuby
[ONNX] Use the torchlib opset number and fix opset import logic (pyto…
efbbced 
…rch#141413)

- Update the ONNX IR `add_opset_imports` pass to remove the heuristics of taking the `max` of the seen opsets. Instead, it uses the torchlib default opset version for the model's opset_import. The version converter is able to take the true opset versions in the nodes and convert the model to the correct version.
- Update all hard coding of opset 18 to instead query the default torchlib opset from onnxscript, introduced in microsoft/onnxscript#1963

Fixes pytorch#141260
Pull Request resolved: pytorch#141413
Approved by: https://github.com/titaiwangms
Esquains pushed a commit to Esquains/study1 that referenced this pull request Dec 15, 2024
@justinchuby
Update on "[ONNX] Use the torchlib opset number and fix opset import …
f8ac3f0 
…logic"


- Update the ONNX IR `add_opset_imports` pass to remove the heuristics of taking the `max` of the seen opsets. Instead, it uses the torchlib default opset version for the model's opset_import. The version converter is able to take the true opset versions in the nodes and convert the model to the correct version.
- Update all hard coding of opset 18 to instead query the default torchlib opset from onnxscript, introduced in microsoft/onnxscript#1963

Fixes pytorch/pytorch#141260

[ghstack-poisoned]
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