[python] Include 'per_channel' attribute when calibrating #19223
Description
Describe the issue
Currently, calibration algorithms compute scalar ranges, i.e. a single value for each calibration tensor. Unfortunately, there is no option for per_channel = True when calibrating.
It would be interesting to have a 'per_channel' option in the initialization parameters of any 'Calibrater' class to produce a set of calibration vectors instead of a single scalar.
Motivation
Some models could improve their performance if the quantization parameters (zero_point/scale) are vectors instead of scalars.
What is expected ?
Include for CalibraterBase class the attribute per_channel = False:
class CalibraterBase:
def __init__(
self,
model,
op_types_to_calibrate: Optional[Sequence[str]] = None,
augmented_model_path="augmented_model.onnx",
symmetric=False,
use_external_data_format=False,
per_channel=False,
):
"""
:param model: ONNX model to calibrate. It can be a ModelProto or a model path
:param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors.
:param augmented_model_path: save augmented model to this path.
:param symmetric: make range of tensor symmetric (central point is 0).
:param use_external_data_format: use external data format to store model which size is >= 2Gb.
:param per_channel: whether to compute range as vector.
"""
...And update calibration algorithms in order to reproduce the new feature.