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Add min max moving average calibration method (#10753)

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* Add min max moving average calibration method

* Modify the calibration extra options dictionnary creation
This commit is contained in:
Ella Charlaix 2022-03-04 23:55:31 +01:00 committed by GitHub
parent 43ff27c7c8
commit fde847473b
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 43 additions and 8 deletions
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37
onnxruntime/python/tools/quantization/calibrate.py
View file

@ -140,19 +140,32 @@ class CalibraterBase:
class MinMaxCalibrater(CalibraterBase):
def __init__(self, model, op_types_to_calibrate=[], augmented_model_path='augmented_model.onnx', symmetric=False, use_external_data_format=False):
def __init__(self,
model,
op_types_to_calibrate=[],
augmented_model_path='augmented_model.onnx',
symmetric=False,
use_external_data_format=False,
moving_average=False,
averaging_constant=0.01):
'''
: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 moving_average: compute the moving average of the minimum and maximum values instead of the global minimum and maximum.
:param averaging_constant: constant smoothing factor to use when computing the moving average.
'''
super(MinMaxCalibrater, self).__init__(model, op_types_to_calibrate, augmented_model_path, symmetric, use_external_data_format)
self.intermediate_outputs = []
self.calibrate_tensors_range = None
self.num_model_outputs = len(self.model.graph.output)
self.model_original_outputs = set(output.name for output in self.model.graph.output)
self.moving_average = moving_average
if moving_average and (averaging_constant < 0 or averaging_constant > 1):
raise ValueError("Invalid averaging constant, which should not be < 0 or > 1.")
self.averaging_constant = averaging_constant
def augment_graph(self):
'''
@ -222,8 +235,12 @@ class MinMaxCalibrater(CalibraterBase):
return new_range
for key, value in old_range.items():
min_value = min(value[0], new_range[key][0])
max_value = max(value[1], new_range[key][1])
if self.moving_average:
min_value = value[0] + self.averaging_constant * (new_range[key][0] - value[0])
max_value = value[1] + self.averaging_constant * (new_range[key][1] - value[1])
else:
min_value = min(value[0], new_range[key][0])
max_value = max(value[1], new_range[key][1])
new_range[key] = (min_value, max_value)
return new_range
@ -258,8 +275,12 @@ class MinMaxCalibrater(CalibraterBase):
for i in range(0, len(added_output_names), 2):
min_value = 0
max_value = 0
min_value_array = min(merged_added_output_dict[added_output_names[i]])
max_value_array = max(merged_added_output_dict[added_output_names[i + 1]])
if self.moving_average:
min_value_array = np.mean(merged_added_output_dict[added_output_names[i]], axis = 0)
max_value_array = np.mean(merged_added_output_dict[added_output_names[i + 1]], axis = 0)
else:
min_value_array = min(merged_added_output_dict[added_output_names[i]])
max_value_array = max(merged_added_output_dict[added_output_names[i + 1]])
if type(min_value_array) == int or min_value_array.size > 0:
min_value = float(min_value_array)
if type(max_value_array) == int or max_value_array.size > 0:
@ -716,10 +737,14 @@ def create_calibrator(model,
if calibrate_method == CalibrationMethod.MinMax:
# default settings for min-max algorithm
symmetric = False if 'symmetric' not in extra_options else extra_options['symmetric']
moving_average = False if 'moving_average' not in extra_options else extra_options['moving_average']
averaging_constant = 0.01 if 'averaging_constant' not in extra_options else extra_options['averaging_constant']
return MinMaxCalibrater(
model, op_types_to_calibrate, augmented_model_path,
use_external_data_format=use_external_data_format,
symmetric=symmetric
symmetric=symmetric,
moving_average=moving_average,
averaging_constant=averaging_constant
)
elif calibrate_method == CalibrationMethod.Entropy:
# default settings for entropy algorithm

14
onnxruntime/python/tools/quantization/quantize.py
View file

@ -226,6 +226,11 @@ def quantize_static(model_input,
If specific op type supports per channel quantization but not explicitly specified with channel axis,
default channel axis will be used.
CalibTensorRangeSymmetric = True/False : Default is False. If enabled, the final range of tensor during calibration will be explicitly set to symmetric to central point "0".
CalibMovingAverage = True/False : Default is False. If enabled, the moving average of the minimum and maximum values
will be computed when the calibration method selected is MinMax.
CalibMovingAverageConstant = float : Default is 0.01. Constant smoothing factor to use when computing the moving average of
the minimum and maximum values. Effective only when the calibration method selected is
MinMax and when CalibMovingAverage is set to True.
'''
mode = QuantizationMode.QLinearOps
@ -235,7 +240,12 @@ def quantize_static(model_input,
model = load_model(Path(model_input), optimize_model, False)
calib_extra_options = {} if 'CalibTensorRangeSymmetric' not in extra_options else {'symmetric': extra_options['CalibTensorRangeSymmetric']}
calib_extra_options_keys = [
('CalibTensorRangeSymmetric', 'symmetric'),
('CalibMovingAverage', 'moving_average'),
('CalibMovingAverageConstant', 'averaging_constant')
]
calib_extra_options = {key: extra_options.get(name) for (name, key) in calib_extra_options_keys if name in extra_options}
calibrator = create_calibrator(
model,
op_types_to_quantize,
@ -355,4 +365,4 @@ def quantize_dynamic(model_input: Path,
extra_options)
quantizer.quantize_model()
quantizer.model.save_model_to_file(model_output, use_external_data_format)
quantizer.model.save_model_to_file(model_output, use_external_data_format)