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calibrate.py
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calibrate.py
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#!/usr/bin/env python
# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft, Intel Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import os
import numpy as np
import onnx
import onnxruntime
from onnx import helper, TensorProto, ModelProto
from onnx import onnx_pb as onnx_proto
from six import string_types
from enum import Enum
from .quant_utils import QuantType, smooth_distribution
from .registry import QLinearOpsRegistry
import abc
import itertools
class CalibrationMethod(Enum):
MinMax = 0
Entropy = 1
class CalibrationDataReader(metaclass=abc.ABCMeta):
@classmethod
def __subclasshook__(cls, subclass):
return (hasattr(subclass, 'get_next') and callable(subclass.get_next) or NotImplemented)
@abc.abstractmethod
def get_next(self) -> dict:
"""generate the input data dict for ONNXinferenceSession run"""
raise NotImplementedError
class CalibraterBase:
def __init__(self, model, op_types_to_calibrate=[], augmented_model_path='augmented_model.onnx'):
'''
: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.
'''
if isinstance(model, string_types):
self.model = onnx.load(model)
elif isinstance(model, ModelProto):
self.model = model
else:
raise ValueError('model should be either model path or onnx.ModelProto.')
self.op_types_to_calibrate = op_types_to_calibrate
self.augmented_model_path = augmented_model_path
# augment graph
self.augment_model = None
self.augment_graph()
# Create InferenceSession
self.infer_session = None
self.execution_providers = ['CPUExecutionProvider']
self._create_inference_session()
def set_execution_providers(self, execution_providers=['CPUExecutionProvider']):
'''
reset the execution providers to execute the collect_data. It triggers to re-creating inference session.
'''
self.execution_providers = execution_providers
self._create_inference_session()
def _create_inference_session(self):
'''
create an OnnxRuntime InferenceSession.
'''
sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
self.infer_session = onnxruntime.InferenceSession(self.augmented_model_path,
sess_options=sess_options,
providers=self.execution_providers)
def select_tensors_to_calibrate(self, model):
'''
select all quantization_candidates op type nodes' input/output tensors.
returns:
tensors (set): set of tensor name.
value_infos (dict): tensor name to value info.
'''
value_infos = {vi.name: vi for vi in model.graph.value_info}
value_infos.update({ot.name: ot for ot in model.graph.output})
value_infos.update({it.name: it for it in model.graph.input})
initializer = set(init.name for init in model.graph.initializer)
tensors_to_calibrate = set()
tensor_type_to_calibrate = set([TensorProto.FLOAT, TensorProto.FLOAT16])
for node in model.graph.node:
if len(self.op_types_to_calibrate) == 0 or node.op_type in self.op_types_to_calibrate:
for tensor_name in itertools.chain(node.input, node.output):
if tensor_name in value_infos.keys():
vi = value_infos[tensor_name]
if vi.type.HasField('tensor_type') and (
vi.type.tensor_type.elem_type in tensor_type_to_calibrate) and (
tensor_name not in initializer):
tensors_to_calibrate.add(tensor_name)
return tensors_to_calibrate, value_infos
def get_augment_model(self):
'''
return: augmented onnx model
'''
return self.augment_model
def augment_graph(self):
'''
abstract method: augment the input model to prepare for collecting data. It will:
1. save augmented model to augmented_model_path.
2. set the self.augment_model
'''
raise NotImplementedError
def collect_data(self, data_reader: CalibrationDataReader):
'''
abstract method: collect the tensors that will be used for range computation. It can be called multiple times.
'''
raise NotImplementedError
def compute_range(self, data_reader: CalibrationDataReader):
'''
abstract method: compute the [min, max] range for the tensors to calibrate based on the collected data.
'''
raise NotImplementedError
class MinMaxCalibrater(CalibraterBase):
def __init__(self, model, op_types_to_calibrate=[], augmented_model_path='augmented_model.onnx'):
'''
: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.
'''
super(MinMaxCalibrater, self).__init__(model, op_types_to_calibrate, augmented_model_path)
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)
def augment_graph(self):
'''
Adds ReduceMin and ReduceMax nodes to all quantization_candidates op type nodes in
model and ensures their outputs are stored as part of the graph output
:return: augmented ONNX model
'''
model = onnx_proto.ModelProto()
model.CopyFrom(self.model)
model = onnx.shape_inference.infer_shapes(model)
added_nodes = []
added_outputs = []
tensors, value_infos = self.select_tensors_to_calibrate(model)
for tensor in tensors:
# When doing ReduceMax/ReduceMin, keep dimension if tensor contains dim with value of 0,
# for example:
# dim = [ dim_value: 0 ]
#
# otherwise, don't keep dimension.
#
dim = value_infos[tensor].type.tensor_type.shape.dim
keepdims = 0
shape = ()
for d in dim:
# A dimension can be either an integer value or a symbolic variable.
# Dimension with integer value and value of 0 is what we are looking for to keep dimension.
# Please see the def of TensorShapeProto https://github.com/onnx/onnx/blob/master/onnx/onnx.proto#L630
if d.WhichOneof('value') == 'dim_value' and d.dim_value == 0:
keepdims = 1
shape = (1,) if len(dim) == 1 else list(1 for i in range(len(dim)))
break
# Adding ReduceMin nodes
reduce_min_name = tensor + '_ReduceMin'
reduce_min_node = onnx.helper.make_node('ReduceMin', [tensor], [tensor + '_ReduceMin'], reduce_min_name, keepdims=keepdims)
added_nodes.append(reduce_min_node)
added_outputs.append(helper.make_tensor_value_info(reduce_min_node.output[0], TensorProto.FLOAT, shape))
# Adding ReduceMax nodes
reduce_max_name = tensor + '_ReduceMax'
reduce_max_node = onnx.helper.make_node('ReduceMax', [tensor], [tensor + '_ReduceMax'], reduce_max_name, keepdims=keepdims)
added_nodes.append(reduce_max_node)
added_outputs.append(helper.make_tensor_value_info(reduce_max_node.output[0], TensorProto.FLOAT, shape))
model.graph.node.extend(added_nodes)
model.graph.output.extend(added_outputs)
onnx.save(model, self.augmented_model_path)
self.augment_model = model
def clear_collected_data(self):
self.intermediate_outputs = []
def collect_data(self, data_reader: CalibrationDataReader):
while True:
inputs = data_reader.get_next()
if not inputs:
break
self.intermediate_outputs.append(self.infer_session.run(None, inputs))
if len(self.intermediate_outputs) == 0:
raise ValueError("No data is collected.")
self.compute_range()
self.clear_collected_data()
def merge_range(self, old_range, new_range):
if not old_range:
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])
new_range[key] = (min_value, max_value)
return new_range
def compute_range(self):
'''
Compute the min-max range of tensor
:return: dictionary mapping: {added node names: (ReduceMin, ReduceMax) pairs }
'''
if len(self.intermediate_outputs) == 0:
return self.calibrate_tensors_range
output_names = [self.infer_session.get_outputs()[i].name for i in range(len(self.intermediate_outputs[0]))]
output_dicts_list = [
dict(zip(output_names, intermediate_output)) for intermediate_output in self.intermediate_outputs
]
merged_output_dict = {}
for d in output_dicts_list:
for k, v in d.items():
merged_output_dict.setdefault(k, []).append(v)
added_output_names = output_names[self.num_model_outputs:]
calibrate_tensor_names = [
added_output_names[i].rpartition('_')[0] for i in range(0, len(added_output_names), 2)
] #output names
merged_added_output_dict = dict(
(i, merged_output_dict[i]) for i in merged_output_dict if i not in self.model_original_outputs)
pairs = []
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 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:
max_value = float(max_value_array)
pairs.append(tuple([min_value, max_value]))
new_calibrate_tensors_range = dict(zip(calibrate_tensor_names, pairs))
if self.calibrate_tensors_range:
self.calibrate_tensors_range = self.merge_range(self.calibrate_tensors_range, new_calibrate_tensors_range)
else:
self.calibrate_tensors_range = new_calibrate_tensors_range
return self.calibrate_tensors_range
class EntropyCalibrater(CalibraterBase):
def __init__(self, model, op_types_to_calibrate=[], augmented_model_path='augmented_model.onnx'):
'''
: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.
'''
super(EntropyCalibrater, self).__init__(model, op_types_to_calibrate, augmented_model_path)
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.collector = None
def augment_graph(self):
'''
make all quantization_candidates op type nodes as part of the graph output.
:return: augmented ONNX model
'''
model = onnx_proto.ModelProto()
model.CopyFrom(self.model)
model = onnx.shape_inference.infer_shapes(model)
added_nodes = []
added_outputs = []
tensors, value_infos = self.select_tensors_to_calibrate(model)
for tensor in tensors:
added_outputs.append(value_infos[tensor])
model.graph.node.extend(added_nodes)
model.graph.output.extend(added_outputs)
onnx.save(model, self.augmented_model_path)
self.augment_model = model
def clear_collected_data(self):
self.intermediate_outputs = []
def collect_data(self, data_reader: CalibrationDataReader):
'''
Entropy Calibrator collects operators' tensors as well as generates tensor histogram for each operator.
'''
while True:
inputs = data_reader.get_next()
if not inputs:
break
self.intermediate_outputs.append(self.infer_session.run(None, inputs))
if len(self.intermediate_outputs) == 0:
raise ValueError("No data is collected.")
output_names = [self.infer_session.get_outputs()[i].name for i in range(len(self.intermediate_outputs[0]))]
output_dicts_list = [
dict(zip(output_names, intermediate_output)) for intermediate_output in self.intermediate_outputs
]
merged_dict = {}
for d in output_dicts_list:
for k, v in d.items():
merged_dict.setdefault(k, []).append(v)
clean_merged_dict = dict((i, merged_dict[i]) for i in merged_dict if i not in self.model_original_outputs)
if not self.collector:
self.collector = HistogramCollector()
self.collector.collect(clean_merged_dict)
self.clear_collected_data()
def compute_range(self):
'''
Compute the min-max range of tensor
:return: dictionary mapping: {added node names: (ReduceMin, ReduceMax) pairs }
'''
if not self.collector:
raise ValueError("No collector created and can't generate calibration data.")
return self.collector.get_optimal_collection_result()
class CalibrationDataCollector(metaclass=abc.ABCMeta):
"""
Base class for collecting data for calibration-based quantization.
"""
@abc.abstractmethod
def collect(self, name_to_arr):
"""
Generate informative data based on given data.
name_to_arr : dict
tensor name to NDArray data
"""
raise NotImplementedError
@abc.abstractmethod
def get_optimal_collection_result(self):
"""
Get the optimal result among collection data.
"""
raise NotImplementedError
class HistogramCollector(CalibrationDataCollector):
"""
Implementation of collecting histogram data as dict for each tensor targeting on entropy calibration.
ref: https://github.com//apache/incubator-mxnet/blob/master/python/mxnet/contrib/quantization.py
"""
def __init__(self, num_quantized_bins=128):
self.histogram_dict = {}
self.num_quantized_bins= num_quantized_bins
def get_histogram_dict(self):
return self.histogram_dict
def collect(self, name_to_arr):
for tensor, data_arr in name_to_arr.items():
data_arr = np.asarray(data_arr)
data_arr = data_arr.flatten()
if data_arr.size > 0:
min_value = np.min(data_arr)
max_value = np.max(data_arr)
else:
min_value = 0
max_value = 0
threshold = max(abs(min_value), abs(max_value))
if tensor in self.histogram_dict:
old_histogram = self.histogram_dict[tensor]
self.histogram_dict[tensor] = self.merge_histogram(old_histogram, data_arr, min_value, max_value, threshold)
else:
# hist, hist_edges = np.histogram(data_arr, self.num_quantized_bins, range=(min_value, max_value))
hist, hist_edges = np.histogram(data_arr, self.num_quantized_bins, range=(-threshold, threshold))
self.histogram_dict[tensor] = (hist, hist_edges, min_value, max_value, threshold)
def merge_histogram(self, old_histogram, data_arr, new_min, new_max, new_threshold):
(old_hist, old_hist_edges, old_min, old_max, old_threshold) = old_histogram
if new_threshold <= old_threshold:
new_hist, _ = np.histogram(data_arr, len(old_hist), range=(-old_threshold, old_threshold))
return (new_hist + old_hist, old_hist_edges, min(old_min, new_min), max(old_max, new_max), old_threshold)
else:
if old_threshold == 0:
hist, hist_edges = np.histogram(data_arr, new_num_bins, range=(-new_threshold, new_threshold))
hist[len(hist) // 2] += len(old_hist)
else:
old_num_bins = len(old_hist)
old_stride = 2 * old_threshold / old_num_bins
half_increased_bins = int((new_threshold - old_threshold) // old_stride + 1)
new_num_bins = old_num_bins + 2 * half_increased_bins
new_threshold = half_increased_bins * old_stride + old_threshold
hist, hist_edges = np.histogram(data_arr, new_num_bins, range=(-new_threshold, new_threshold))
hist[half_increased_bins:new_num_bins-half_increased_bins] += old_hist
return (hist, hist_edges, min(old_min, new_min), max(old_max, new_max), new_threshold)
def get_optimal_collection_result(self):
histogram_dict = self.histogram_dict
num_quantized_bins = self.num_quantized_bins
thresholds_dict = {} # per tensor thresholds
for tensor, histogram in histogram_dict.items():
optimal_threshold = self.get_optimal_threshold(histogram, num_quantized_bins)
thresholds_dict[tensor] = optimal_threshold
return thresholds_dict
def get_optimal_threshold(self, histogram, num_quantized_bins):
from scipy.stats import entropy
import copy
hist, hist_edges, _, _, _ = histogram
num_bins = hist.size
zero_bin_index = num_bins // 2
num_half_quantized_bin = num_quantized_bins // 2
kl_divergence = np.zeros(zero_bin_index - num_half_quantized_bin + 1)
thresholds = [(0, 0) for i in range(kl_divergence.size)]
for i in range(num_half_quantized_bin, zero_bin_index + 1, 1):
start_index = zero_bin_index - i
end_index = zero_bin_index + i + 1 if (zero_bin_index + i + 1) <= num_bins else num_bins
thresholds[i - num_half_quantized_bin] = (float(hist_edges[start_index]), float(hist_edges[end_index]))
sliced_distribution = copy.deepcopy(hist[start_index:end_index])
# reference distribution p
p = sliced_distribution.copy() # a copy of np array
left_outliers_count = sum(hist[:start_index])
right_outliers_count = sum(hist[end_index:])
p[0] += left_outliers_count
p[-1] += right_outliers_count
# nonzeros[i] incidates whether p[i] is non-zero
nonzeros = (p != 0).astype(np.int64)
# quantize p.size bins into quantized bins (default 128 bins)
quantized_bins = np.zeros(num_quantized_bins, dtype=np.int64)
num_merged_bins = sliced_distribution.size // num_quantized_bins
# merge bins into quantized bins
for index in range(num_quantized_bins):
start = index * num_merged_bins
end = start + num_merged_bins
quantized_bins[index] = sum(sliced_distribution[start:end])
quantized_bins[-1] += sum(sliced_distribution[num_quantized_bins * num_merged_bins:])
# in order to compare p and q, we need to make length of q equals to length of p
# expand quantized bins into p.size bins
q = np.zeros(p.size, dtype=np.int64)
for index in range(num_quantized_bins):
start = index * num_merged_bins
end = start + num_merged_bins
norm = sum(nonzeros[start:end])
if norm != 0:
q[start:end] = float(quantized_bins[index]) / float(norm)
p = smooth_distribution(p)
q = smooth_distribution(q)
if isinstance(q, np.ndarray):
kl_divergence[i - num_half_quantized_bin] = entropy(p, q)
else:
kl_divergence[i - num_half_quantized_bin] = float('inf')
min_kl_divergence_idx = np.argmin(kl_divergence)
optimal_threshold = thresholds[min_kl_divergence_idx]
return optimal_threshold
def create_calibrator(model,
op_types_to_calibrate=[],
augmented_model_path='augmented_model.onnx',
calibrate_method=CalibrationMethod.MinMax):
if calibrate_method == CalibrationMethod.MinMax:
return MinMaxCalibrater(model, op_types_to_calibrate, augmented_model_path)
elif calibrate_method == CalibrationMethod.Entropy:
return EntropyCalibrater(model, op_types_to_calibrate, augmented_model_path)
raise ValueError('Unsupported calibration method {}'.format(calibrate_method))