onnx_diagnostic.ci_models.ci_helpers

onnx_diagnostic.ci_models.ci_helpers.check_for_discrepancies_and_log_everything_into_a_json_file(agg_stat_file: str, stat_file: str, export_duration: float, device: str, model_file: str, cached_inputs: str, cached_expected_outputs: str, main_info: Dict[str, Any], atol: float, mismatch01: float)

Checks discrepancies for a specific model.

Imports are delayed to be faster when running the help of the command line.

Parameters:

• agg_stat_file – a file when the discrepancies are collected, this is used to produce a table to make it easier to compare across types, devices, …

• stat_file – discrepancies results dumps into that file

• export_duration – export duration

• device – targeted device (to select onnxruntime provider)

• model_file – onnx model file

• cache_inputs – inputs saved with torch.save() and restored with torch.load(), needs to contains export_inputs (to check the model is valid), and other_inputs, other sets of inputs to measure the discrepancies, and speed up (rough estimation)

• cached_expected_outputs – expected outputs saved with torch.save() and restored with torch.load(), needs to contains export_expected (to check the model is valid), and other_expected, other sets of outputs to measure the discrepancies, and speed up (rough estimation)

• main_info – a dictionary with values used to tell which version, device, …

• atol – assert if tolerance is above this

• mismatch01 – assert if the ratio of mismatches is above that threshold

onnx_diagnostic.ci_models.ci_helpers.compute_expected_outputs(output_filename: str, model_to_export: torch.nn.Module, input_filename: str) → Tuple[Any, List[Any], List[float]]

Computes the expected outputs for a model. The function uses delayed import to make to fail fast at startup.

It caches the expected outputs in a file. They are restored if the file exists or computed and saved if not.

Imports are delayed to be faster when running the help of the command line.

onnx_diagnostic.ci_models.ci_helpers.get_parser(name: str) → ArgumentParser

Creates a default parser for many models.

onnx_diagnostic.ci_models.ci_helpers.get_torch_dtype_from_command_line_args(dtype: str) → torch.dtype

Returns the torch dtype base on the argument provided on the command line.

Imports are delayed to be faster when running the help of the command line.

onnx_diagnostic.ci_models.ci_helpers.get_versions()

Returns the version of the package currently used. The output is a dictionary. The function uses delayed import to make to fail fast at startup.

onnx_diagnostic.ci_models.ci_helpers.remove_inplace_body_last_input_output_type_for_loop_because_they_might_be_sequences(filename: str)

Modified inplace an onnx file. It wipes out shapes provided in model.graph.value_info because they are wrong when a Loop outputs a sequence. It alose removes the types in attribute ‘Body’ of an operator Loop because it may be a tensor when a sequence is expected. This should not be needed in the future.

onnx_diagnostic.ci_models.ci_helpers.simplify_model_id_for_a_filename(model_id: str) → str

Changes a model id in a way it can be used in a filename.

onnx_diagnostic.ci_models.ci_helpers.zip_model_and_data_into_a_single_file(zip_file: str, model_file: str)

Zips an onnx model and its data into a zingle file.

Parameters:

• zip_file – zip file to create

• model_file – onnx file