experimental_experiment.bench_run¶

exception experimental_experiment.bench_run.BenchmarkError[source]¶

experimental_experiment.bench_run.flatten_object(x: Any) → List[Any][source]¶: Flatten the object.

experimental_experiment.bench_run.get_machine(capability_as_str: bool = True) → Dict[str, str | int | float | Tuple[int, int]][source]¶: Returns the machine specifications.

experimental_experiment.bench_run.get_processor_name()[source]¶: Returns the processor name.

experimental_experiment.bench_run.make_configs(kwargs: Namespace | Dict[str, Any], drop: Set[str] | None = None, replace: Dict[str, str] | None = None, last: List[str] | None = None, filter_function: Callable[[Dict[str, Any]], bool] | None = None) → List[Dict[str, Any]][source]¶

Creates all the configurations based on the command line arguments.

Parameters:

kwargs – parameters the command line, every value having a comma means multiple values, it multiplies the number of configurations to try by the number of comma separated values
drop – keys to drop in kwargs if specified
replace – values to replace for a particular key
last – to change the order of the loop created the configuration, if last == ["part"] and kwargs[part] == "0,1", then configuration where part==0 is always followed by a configuration having part==1
filter_function – function taking a configuration and returning True if it is must be kept

Returns:

list of configurations

experimental_experiment.bench_run.make_dataframe_from_benchmark_data(data: List[Dict], detailed: bool = True, string_limit: int = 2000) → Any[source]¶

Creates a dataframe from the received data.

Parameters:

data – list of dictionaries for every run
detailed – remove multi line and long values
string_limit – truncate the strings

Returns:

dataframe

experimental_experiment.bench_run.max_diff(expected: Any, got: Any, verbose: int = 0, level: int = 0, flatten: bool = False, debug_info: List[str] | None = None, begin: int = 0, end: int = -1, _index: int = 0) → Dict[str, float][source]¶

Returns the maximum discrepancy.

Parameters:

expected – expected values
got – values
verbose – verbosity level
level – for embedded outputs, used for debug purpposes
flatten – flatten outputs
debug_info – debug information
begin – first output to considered
end – last output to considered (-1 for the last one)
_index – used with begin and end

Returns:

dictionary with many values

abs: max abolute error
rel: max relative error
sum: sum of the errors
n: number of outputs values, if there is one
output, this number will be the number of elements of this output

experimental_experiment.bench_run.measure_discrepancies(expected: List[Tuple[torch.Tensor, ...]], outputs: List[Tuple[torch.Tensor, ...]]) → Dict[str, float][source]¶

Computes the discrepancies.

Parameters:

expected – list of outputs coming from a torch model
outputs – list of outputs coming from an onnx model

Returns:

dictionary with max absolute errors, max relative errors, sum of absolute error, the number of elements contributing to it

experimental_experiment.bench_run.multi_run(kwargs: Namespace) → bool[source]¶: Checks if multiple values were sent for one argument.

Runs a script multiple times and extract information from the output following the pattern :<metric>,<value>;.

Parameters:

script_name – python script to run
configs – list of execution to do
stop_if_exception – stop if one experiment failed, otherwise continue
verbose – use tqdm to follow the progress
dump – dump onnx file, sets variable ONNXRT_DUMP_PATH
temp_output_data – to save the data after every run to avoid losing data
dump_std – dumps stdout and stderr in this folder
start – start at this iteration
summary – function to call on the temporary data and the final data
timeout – timeout for the subprocesses
missing – populate with this missing value if not found

Returns:

values