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[ONNX] Documentation for torch.onnx.find_mismatch (#90728)
Doc preview: * `find_mismatch`: https://docs-preview.pytorch.org/90728/onnx.html#torch.onnx.verification.find_mismatch * `GraphInfo`: https://docs-preview.pytorch.org/90728/onnx.html#classes and https://docs-preview.pytorch.org/90728/generated/torch.onnx.verification.GraphInfo.html#torch.onnx.verification.GraphInfo * `VerificationOptions`: https://docs-preview.pytorch.org/90728/onnx.html#classes and https://docs-preview.pytorch.org/90728/generated/torch.onnx.verification.VerificationOptions.html#torch.onnx.verification.VerificationOptions Pull Request resolved: https://github.com/pytorch/pytorch/pull/90728 Approved by: https://github.com/titaiwangms, https://github.com/justinchuby
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BowenBao
PyTorch MergeBot
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@ -706,6 +706,7 @@ Functions
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.. autofunction:: is_in_onnx_export
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.. autofunction:: enable_log
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.. autofunction:: disable_log
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.. autofunction:: torch.onnx.verification.find_mismatch
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Classes
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-------
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@ -716,3 +717,5 @@ Classes
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:template: classtemplate.rst
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JitScalarType
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torch.onnx.verification.GraphInfo
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torch.onnx.verification.VerificationOptions
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@ -681,6 +681,9 @@ def _onnx_graph_from_aten_graph(
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do_constant_folding = export_options.do_constant_folding
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opset_version = export_options.opset_version or _constants.ONNX_DEFAULT_OPSET
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GLOBALS.export_onnx_opset_version = opset_version
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GLOBALS.operator_export_type = operator_export_type
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do_constant_folding = utils._decide_constant_folding(
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do_constant_folding, operator_export_type, training
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)
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@ -816,7 +819,7 @@ def verify(
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] = None,
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input_names: Optional[Sequence[str]] = None,
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output_names: Optional[Sequence[str]] = None,
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training: torch.onnx.TrainingMode = torch.onnx.TrainingMode.EVAL,
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training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
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opset_version: Optional[int] = None,
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keep_initializers_as_inputs: bool = True,
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verbose: bool = False,
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@ -1197,6 +1200,8 @@ class OnnxTestCaseRepro:
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@dataclasses.dataclass
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class GraphInfo:
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"""GraphInfo contains validation information of a TorchScript graph and its converted ONNX graph."""
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graph: torch.Graph
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input_args: Tuple[Any, ...]
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params_dict: Dict[str, Any]
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@ -1235,8 +1240,8 @@ class GraphInfo:
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The id of the subgraph is shown under the node. The `GraphInfo` object for any
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subgraph can be retrieved by calling `graph_info.find_partition(id)`.
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Example:
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```
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Example::
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==================================== Tree: =====================================
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5 X __2 X __1 ✓
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id: | id: 0 | id: 00
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@ -1256,7 +1261,7 @@ class GraphInfo:
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['01', '110']
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============================= Mismatch node kinds: =============================
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{'aten::relu': 2}
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```
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"""
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GraphInfoPrettyPrinter(self).pretty_print()
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@ -1345,7 +1350,7 @@ class GraphInfo:
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) -> str:
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"""Export the subgraph to ONNX along with the input/output data for repro.
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The repro directory will contain the following files:
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The repro directory will contain the following files::
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dir
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├── test_<name>
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@ -1612,6 +1617,24 @@ class GraphInfo:
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def verify_export(
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self, options: VerificationOptions
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) -> Tuple[Optional[AssertionError], torch.Graph, _OutputsType, _OutputsType]:
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"""
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Verify the export from TorchScript IR graph to ONNX.
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Export the TorchScript IR graph to ONNX, with the inputs, parameters and export
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options recorded in this object. Then verify the exported ONNX graph against
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the original TorchScript IR graph under the provided verification options.
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Args:
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options: The verification options.
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Returns:
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error: The AssertionError raised during the verification. Returns None if no
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error is raised.
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onnx_graph: The exported ONNX graph in TorchScript IR format.
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onnx_outs: The outputs from running exported ONNX model under the onnx
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backend in `options`.
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pt_outs: The outputs from running the TorchScript IR graph.
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"""
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return verify_aten_graph(
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self.graph,
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input_args=self.input_args,
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@ -1625,6 +1648,16 @@ class GraphInfo:
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self,
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options: Optional[VerificationOptions] = None,
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):
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"""
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Find all mismatches between the TorchScript IR graph and the exported onnx model.
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Binary searches the model graph to find the minimal subgraph that exhibits the
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mismatch. A `GraphInfo` object is created for each subgraph, recording the test
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inputs and export options, as well as the validation results.
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Args:
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options: The verification options.
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"""
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self.clear()
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if options is None:
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@ -1724,26 +1757,92 @@ def find_mismatch(
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model: Union[torch.nn.Module, torch.jit.ScriptModule],
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input_args: Tuple[Any, ...],
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do_constant_folding: bool = True,
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training: torch.onnx.TrainingMode = torch.onnx.TrainingMode.EVAL,
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training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
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opset_version: Optional[int] = None,
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keep_initializers_as_inputs: bool = True,
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verbose: bool = False,
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options: Optional[VerificationOptions] = None,
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) -> GraphInfo:
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r"""Find all mismatches between the original model and the exported model.
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TODO: Fill in docstring.
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Experimental. The API is subject to change.
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This tool helps debug the mismatch between the original PyTorch model and exported
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ONNX model. It binary searches the model graph to find the minimal subgraph that
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exhibits the mismatch.
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Args:
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model: The model to be exported.
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input_args: The input arguments to the model.
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do_constant_folding: Same as `do_constant_folding` in :func:`torch.onnx.export`.
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training: Same as `training` in :func:`torch.onnx.export`.
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opset_version: Same as `opset_version` in :func:`torch.onnx.export`.
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keep_initializers_as_inputs: Same as `keep_initializers_as_inputs` in :func:`torch.onnx.export`.
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verbose: Same as `verbose` in :func:`torch.onnx.export`.
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options: The options for the mismatch verification.
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Returns:
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A GraphInfo object that contains the mismatch information.
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Example::
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>>> import torch
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>>> import torch.onnx.verification
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>>> torch.manual_seed(0)
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>>> opset_version = 15
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>>> # Define a custom symbolic function for aten::relu.
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>>> # The custom symbolic function is incorrect, which will result in mismatches.
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>>> def incorrect_relu_symbolic_function(g, self):
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... return self
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>>> torch.onnx.register_custom_op_symbolic(
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... "aten::relu",
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... incorrect_relu_symbolic_function,
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... opset_version=opset_version,
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... )
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>>> class Model(torch.nn.Module):
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... def __init__(self):
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... super().__init__()
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... self.layers = torch.nn.Sequential(
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... torch.nn.Linear(3, 4),
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... torch.nn.ReLU(),
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... torch.nn.Linear(4, 5),
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... torch.nn.ReLU(),
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... torch.nn.Linear(5, 6),
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... )
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... def forward(self, x):
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... return self.layers(x)
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>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
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>>> graph_info = torch.onnx.verification.find_mismatch(
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... Model(),
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... (torch.randn(2, 3),),
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... opset_version=opset_version,
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... )
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===================== Mismatch info for graph partition : ======================
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================================ Mismatch error ================================
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Tensor-likes are not close!
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Mismatched elements: 12 / 12 (100.0%)
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Greatest absolute difference: 0.2328854203224182 at index (1, 2) (up to 1e-07 allowed)
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Greatest relative difference: 0.699536174352349 at index (1, 3) (up to 0.001 allowed)
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==================================== Tree: =====================================
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5 X __2 X __1 ✓
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id: | id: 0 | id: 00
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| |
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| |__1 X (aten::relu)
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| id: 01
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|__3 X __1 ✓
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id: 1 | id: 10
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|__2 X __1 X (aten::relu)
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id: 11 | id: 110
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|__1 ✓
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id: 111
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=========================== Mismatch leaf subgraphs: ===========================
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['01', '110']
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============================= Mismatch node kinds: =============================
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{'aten::relu': 2}
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"""
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if options is None:
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options = VerificationOptions()
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