.torch_interpreter.investigate_helper

experimental_experiment.torch_interpreter.investigate_helper.prepare_args_kwargs(torch_results: Dict[str, Any], node: Node) → Tuple[Tuple[Any, ...], Dict[str, Any]]

Prepares args and kwargs before executing a fx node.

Parameters:

• torch_results – existing results

• node – node to execute

Returns:

new args and kwargs

experimental_experiment.torch_interpreter.investigate_helper.run_aligned(ep: ExportedProgram, onx: ModelProto | FunctionProto, args: Tuple[Tensor, ...], check_conversion_cls: Dict[str, Any] | type, kwargs: Dict[str, Any] | None = None, verbose: int = 0) → Iterator[Tuple[Any, ...]]

Runs both the exported program and the onnx proto and looks for discrepancies.

Parameters:

• ep – exported program

• onx – model or function proto

• args – input args

• check_conversion_cls – defines the runtime to use for this task

• kwargs – input kwargs

• verbose – verbosity level

Returns:

a list of tuples containing the results.

experimental_experiment.torch_interpreter.investigate_helper.run_fx_node(node: Node, args: Tuple[Any, ...], kwargs: Dict[str, Any] | None = None) → Tuple[Any, ...]

Executes a node

Parameters:

• node – runs a node

• args – unnamed inputs to the node

• kwargs – named inputs to the node

Returns:

results

experimental_experiment.torch_interpreter.investigate_helper.validate_fx_outputs(node: Node, outputs: Tuple[Any, ...]) → None

Validates the outputs of a node using metadata stored in the node.

Parameters:

• node – node

• outputs – outputs

experimental_experiment.torch_interpreter.investigate_helper.validate_fx_tensor(node: Node, tensor: Tensor, expected_shape: Tuple[Any, ...]) → None

Validates the shape of tensor is expected.

Parameters:

• node – node

• tensor – tensor

• expected_shape – expected shape