post_training_quantization.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import os
import re
import logging
import numpy as np
import shutil
from inspect import isgeneratorfunction
from .... import io
from .... import core
from .... import framework
from .... import unique_name
from ....executor import global_scope, Executor
from ....framework import IrGraph
from ....log_helper import get_logger
from .quantization_pass import QuantizationTransformPass, QuantizationTransformPassV2, QuantizationFreezePass, QuantWeightPass, AddQuantDequantPass, AddQuantDequantPassV2
from .cal_kl_threshold import cal_kl_threshold
from .adaround import run_adaround
from . import utils

__all__ = ['PostTrainingQuantization', 'WeightQuantization']

_logger = get_logger(
    __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')


def _all_persistable_var_names(program):
    persistable_var_names = []
    for var in program.list_vars():
        if var.persistable:
            persistable_var_names.append(var.name)
    return persistable_var_names


def _remove_unused_var_nodes(graph):
    all_used_vars = set()
    ops = graph.all_op_nodes()
    for op_node in ops:
        for input_node in op_node.inputs:
            all_used_vars.add(input_node)
        for output_node in op_node.outputs:
            all_used_vars.add(output_node)

    all_used_vars = {n.node for n in all_used_vars}
    all_unused_vars = {
        n
        for n in filter(lambda node: node.node not in all_used_vars,
                        graph.all_var_nodes())
    }
    graph.safe_remove_nodes(all_unused_vars)
    return graph


def _remove_ctrl_vars(graph):
    remove_ctr_vars = set()
    for node in graph.all_var_nodes():
        if node.is_ctrl_var():
            remove_ctr_vars.add(node)
    graph.safe_remove_nodes(remove_ctr_vars)
    return graph


def _apply_pass(scope,
                graph,
                pass_name,
                attrs=None,
                attr_values=None,
                debug=False):
    ir_pass = core.get_pass(pass_name)
    cpp_graph = graph.graph
    if not cpp_graph.has('__param_scope__'):
        cpp_graph.set_not_owned('__param_scope__', scope)
    if attrs:
        assert attr_values and len(attrs) == len(
            attr_values), "Different number of pass attributes and their values."
        for attr, value in zip(attrs, attr_values):
            ir_pass.set(attr, value)
    ir_pass.apply(cpp_graph)
    if debug:
        graph.draw('.', 'qat_fp32_{}'.format(pass_name), graph.all_op_nodes())
    _remove_unused_var_nodes(graph)
    return graph


class PostTrainingQuantization(object):
    """
    Utilizing post training quantization methon to quantize the FP32 model,
    and it uses calibrate data to get the quantization information for all 
    quantized variables.
    """

    def __init__(self,
                 executor=None,
                 scope=None,
                 model_dir=None,
                 model_filename=None,
                 params_filename=None,
                 batch_generator=None,
                 sample_generator=None,
                 data_loader=None,
                 batch_size=10,
                 batch_nums=None,
                 algo="KL",
                 hist_percent=0.99999,
                 quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
                 round_type='round',
                 learning_rate=0.001,
                 is_full_quantize=False,
                 bias_correction=False,
                 activation_bits=8,
                 weight_bits=8,
                 activation_quantize_type='range_abs_max',
                 weight_quantize_type='channel_wise_abs_max',
                 onnx_format=False,
                 optimize_model=False,
                 is_use_cache_file=False,
                 cache_dir=None):
        '''
        Constructor.

        Args:
            executor(fluid.Executor): The executor to load, run and save the
                quantized model.
            scope(fluid.Scope, optional): The scope of the program, use it to load 
                and save variables. If scope=None, get scope by global_scope(). 
            model_dir(str): The path of the fp32 model that will be quantized, 
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference 
                program. If it is None, the default filename '__model__' will 
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it 
                as the real filename. If parameters were saved in separate files, 
                set it as 'None'. Default is 'None'.
            batch_generator(Python Generator): The batch generator provides 
                calibrate data for DataLoader, and it returns a batch every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, batch_generator supports lod tensor.
            sample_generator(Python Generator): The sample generator provides
                calibrate data for DataLoader, and it only returns a sample every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, sample_generator dose not support lod tensor.
            data_loader(Python Generator, Paddle.io.DataLoader, optional): The
                Generator or Dataloader provides calibrate data, and it could
                return a batch every time.
            batch_size(int, optional): The batch size of DataLoader. Default is 10.
            batch_nums(int, optional): If batch_nums is not None, the number of 
                calibrate data is batch_size*batch_nums. If batch_nums is None, use 
                all data provided by sample_generator as calibrate data.
            algo(str, optional): If algo='KL', use KL-divergenc method to
                get the KL threshold for quantized activations and get the abs_max
                value for quantized weights. If algo='abs_max', get the abs max 
                value for activations and weights. If algo= 'min_max', get the min 
                and max value for quantized activations and weights. If algo='avg',
                get the average value among the max values for activations. If 
                algo= 'hist', get the value of 'hist_percent' quantile as the threshold.
                If algo='mse', get the value which makes the quantization mse loss 
                minimal. Default is KL.
            hist_percent(float, optional): The threshold of algo 'hist' for activations.
                Default is 0.99999.
            quantizable_op_type(list[str], optional): List the type of ops 
                that will be quantized. Default is ["conv2d", "depthwise_conv2d", 
                "mul"].
            round_type(str, optional): The method of converting the quantized weights
                value float->int. Currently supports ['round', 'adaround'] methods.
                Default is `round`, which is rounding nearest to the nearest whole number.
            learning_rate(float, optional): The learning rate of adaround method.
            is_full_quantized(bool, optional): If set is_full_quantized as True, 
                apply quantization to all supported quantizable op type. If set
                is_full_quantized as False, only apply quantization to the op type 
                according to the input quantizable_op_type.
            bias_correction(bool, optional): If set as True, use the bias correction
                method of https://arxiv.org/abs/1810.05723. Default is False.
            activation_bits(int): quantization bit number for activation.
            weight_bits(int, optional): quantization bit number for weights.
            activation_quantize_type(str): quantization type for activation,
                now support 'range_abs_max', 'moving_average_abs_max' and 'abs_max'.
                This param only specifies the fake ops in saving quantized model.
                If it is 'range_abs_max' or 'moving_average_abs_max', we save the scale
                obtained by post training quantization in fake ops. Note that, if it
                is 'abs_max', the scale will not be saved in fake ops.
            weight_quantize_type(str): quantization type for weights,
                support 'abs_max' and 'channel_wise_abs_max'. This param only specifies
                the fake ops in saving quantized model, and we save the scale obtained
                by post training quantization in fake ops. Compared to 'abs_max',
                the model accuracy is usually higher when it is 'channel_wise_abs_max'.
            onnx_format(bool): Whether to export the quantized model with format of ONNX.
                Default is False.
            optimize_model(bool, optional): If set optimize_model as True, it applies
                some passes to the model before quantization, and it supports
                `conv2d/depthwise_conv2d + bn` pass so far. Some targets require the
                weights are quantized by tensor-wise method, which means the weights
                scale for all channel are the same. However, if fuse
                `conv2d/depthwise_conv2d + bn`, the weights scale for all channel will
                be different. In address this problem, fuse the pattern before
                quantization. Default False.
            is_use_cache_file(bool, optional): This param is deprecated.
            cache_dir(str, optional): This param is deprecated.
        Returns:
            None

        Examples:
        .. code-block:: python
            import paddle.fluid as fluid
            from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
            
            exe = fluid.Executor(fluid.CPUPlace())
            model_dir = path/to/fp32_model_params
            # set model_filename as None when the filename is __model__, 
            # otherwise set it as the real filename
            model_filename = None 
            # set params_filename as None when all parameters were saved in 
            # separate files, otherwise set it as the real filename
            params_filename = None
            save_model_path = path/to/save_model_path
            # prepare the sample generator according to the model, and the 
            # sample generator must return a sample every time. The reference
            # document: https://www.paddlepaddle.org.cn/documentation/docs/zh
            # /user_guides/howto/prepare_data/use_py_reader.html
            sample_generator = your_sample_generator
            batch_size = 10
            batch_nums = 10
            algo = "KL"
            quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
            ptq = PostTrainingQuantization(
                        executor=exe,
                        sample_generator=sample_generator,
                        model_dir=model_dir,
                        model_filename=model_filename,
                        params_filename=params_filename,
                        batch_size=batch_size,
                        batch_nums=batch_nums,
                        algo=algo,
                        quantizable_op_type=quantizable_op_type)
            ptq.quantize()
            ptq.save_quantized_model(save_model_path)
        '''

        self._support_activation_quantize_type = [
            'range_abs_max', 'moving_average_abs_max', 'abs_max'
        ]
        self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
        self._support_algo_type = [
            'KL', 'hist', 'avg', 'mse', 'emd', 'abs_max', 'min_max'
        ]
        assert round_type in ['adaround', 'round']
        self._round_type = round_type
        self._learning_rate = learning_rate
        self._dynamic_quantize_op_type = ['lstm']
        self._support_quantize_op_type = \
            list(set(utils._weight_supported_quantizable_op_type +
                utils._act_supported_quantizable_op_type +
                self._dynamic_quantize_op_type))

        # Check inputs
        assert executor is not None, "The executor cannot be None."
        assert model_dir is not None, "The model_dir cannot be None."
        assert any([gen is not None] for gen in [sample_generator,
            batch_generator, data_loader]), "The sample_generator, batch_generator " \
            "and data_loader cannot be None in the same time."
        if data_loader is not None:
            assert isinstance(data_loader, (io.DataLoader, type(isgeneratorfunction))), \
                "data_loader only accepts `paddle.io.DataLoader` or Generator instance."
        assert batch_size > 0, "The batch_size should be greater than 0."
        assert algo in self._support_algo_type, \
            "The algo should be KL, hist, mse, avg, abs_max or min_max."
        assert activation_quantize_type in self._support_activation_quantize_type, \
            "The activation_quantize_type ({}) should in ({}).".format(
            activation_quantize_type, self._support_activation_quantize_type)
        assert weight_quantize_type in self._support_weight_quantize_type, \
            "The weight_quantize_type ({}) shoud in ({}).".format(
            weight_quantize_type, self._support_weight_quantize_type)

        # Save input params
        self._bias_correction = bias_correction
        self._executor = executor
        self._scope = global_scope() if scope == None else scope
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename
        self._sample_generator = sample_generator
        self._batch_generator = batch_generator
        self._batch_size = batch_size
        self._batch_nums = batch_nums
        self._algo = algo
        self._hist_percent = hist_percent
        self._activation_bits = activation_bits
        self._weight_bits = weight_bits
        self._activation_quantize_type = activation_quantize_type
        self._weight_quantize_type = weight_quantize_type
        self._onnx_format = onnx_format
        self._is_full_quantize = is_full_quantize
        if is_full_quantize:
            self._quantizable_op_type = self._support_quantize_op_type
        else:
            self._quantizable_op_type = quantizable_op_type
            for op_type in self._quantizable_op_type:
                assert op_type in self._support_quantize_op_type, \
                    op_type + " is not supported for quantization."
        self._optimize_model = optimize_model

        # Define variables
        self._place = self._executor.place
        self._program = None
        self._feed_list = None
        self._fetch_list = None
        self._data_loader = data_loader

        self._out_scale_op_list = utils._out_scale_op_list
        self._quantized_weight_var_name = set()
        self._quantized_act_var_name = set()
        self._weight_op_pairs = {}
        # The vars for alog = KL or hist
        self._sampling_act_abs_min_max = {}
        self._sampling_act_histogram = {}
        self._sampling_data = {}
        self._quantized_var_threshold = {}
        self._histogram_bins = 2048
        # The vars for algo = min_max
        self._quantized_var_min = {}
        self._quantized_var_max = {}
        # The vars for algo = avg
        self._quantized_var_avg = {}
        # The best loss of algo = mse
        self._best_calibration_loss = {}
        # The threshold for algo = abs_max, mse or avg
        self._quantized_threshold = {}

    def quantize(self):
        '''
        Load the FP32 model, and use the calibrate data to calculate the forward-stage.
        Based on the sample data, we can get the quantization information, and obtain
        the final quantized model.

        Args:
            None
        Returns:
            the program of quantized model.
        '''
        self._load_model_data()
        self._collect_target_varnames()
        self._set_activation_persistable()

        if self._algo in ["KL", "hist"]:
            _logger.info("Preparation stage ...")
            batch_id = 0
            for data in self._data_loader():
                self._executor.run(program=self._program,
                                   feed=data,
                                   fetch_list=self._fetch_list,
                                   return_numpy=False,
                                   scope=self._scope)
                self._collect_activation_abs_min_max()
                if batch_id % 5 == 0:
                    _logger.info("Run batch: " + str(batch_id))
                batch_id += 1
                if self._batch_nums and batch_id >= self._batch_nums:
                    break
            _logger.info("Finish preparation stage, all batch:" + str(batch_id))
            self._init_sampling_act_histogram()

        _logger.info("Sampling stage ...")
        batch_id = 0
        for data in self._data_loader():
            self._executor.run(program=self._program,
                               feed=data,
                               fetch_list=self._fetch_list,
                               return_numpy=False,
                               scope=self._scope)
            self._sampling()
            if batch_id % 5 == 0:
                _logger.info("Run batch: " + str(batch_id))
            batch_id += 1
            if self._batch_nums and batch_id >= self._batch_nums:
                break
        _logger.info("Finish sampling stage, all batch: " + str(batch_id))

        if self._algo == 'avg':
            for var_name in self._quantized_act_var_name:
                self._quantized_threshold[var_name] = \
                np.array(self._quantized_var_avg[var_name]).mean()
        if self._algo in ["KL", "hist"]:
            self._calculate_kl_hist_threshold()

        if self._round_type == 'adaround':
            self._adaround_apply()

        self._reset_activation_persistable()

        if self._algo is 'min_max':
            self._save_input_threhold()
        else:
            self._update_program()

        # save out_threshold for quantized ops.
        if not self._onnx_format:
            self._save_output_threshold()

        if any(op_type in self._quantizable_op_type
               for op_type in self._dynamic_quantize_op_type):
            self._collect_dynamic_quantize_op_threshold(
                self._dynamic_quantize_op_type)

        # Move sub blocks persistable var to global block
        global_block = self._program.global_block()
        for _op in global_block.ops:
            if _op.type == "while":
                _block_id = _op.attr("sub_block").id
                _block = self._program.block(_block_id)
                persistables = []
                for _name, _var in _block.vars.items():
                    if _var.persistable:
                        global_block._clone_variable(_var)
                        persistables.append(_name)
                for _name in persistables:
                    _block._remove_var(_name)
                persistables.extend(_op.input('X'))
                _op.desc.set_input("X", persistables)

        return self._program

    def _adaround_apply(self):
        assert self._algo != "min_max", "The algo should not be min_max."
        if self._algo in ["KL", "hist"]:
            scale_dict = self._quantized_var_threshold
        else:
            scale_dict = self._quantized_threshold
        run_adaround(
            self._data_loader,
            self._program,
            self._fetch_list,
            self._executor,
            self._scope,
            self._place,
            self._quantized_op_pairs,
            self._weight_op_pairs,
            scale_dict,
            num_iterations=self._batch_nums,
            lr=self._learning_rate)

    def save_quantized_model(self,
                             save_model_path,
                             model_filename=None,
                             params_filename=None):
        '''
        Save the quantized model to the disk.

        Args:
            save_model_path(str): The path to save the quantized model.
            model_filename(str, optional): If the model_filename is None,
                save the model to '__model__'. Otherwise, save the model
                to the specified filename. Default: None.
            params_filename(str, optional): If the params_filename is None,
                save params to separted files. Otherwise, save all params
                to the specified filename.
        Returns:
            None
        '''
        clip_extra = True if self._onnx_format else False
        io.save_inference_model(
            dirname=save_model_path,
            model_filename=model_filename,
            params_filename=params_filename,
            feeded_var_names=self._feed_list,
            target_vars=self._fetch_list,
            executor=self._executor,
            main_program=self._program,
            clip_extra=clip_extra)
        _logger.info("The quantized model is saved in " + save_model_path)

    def _load_model_data(self):
        '''
        Load model and set data loader.
        '''
        _logger.info("Load model and set data loader ...")
        [self._program, self._feed_list, self._fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=self._executor,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        if self._optimize_model:
            self._optimize_fp32_model()

        feed_vars = [framework._get_var(str(var_name), self._program) \
            for var_name in self._feed_list]

        if self._data_loader is not None:
            return
        self._data_loader = io.DataLoader.from_generator(
            feed_list=feed_vars, capacity=3 * self._batch_size, iterable=True)
        if self._sample_generator is not None:
            self._data_loader.set_sample_generator(
                self._sample_generator,
                batch_size=self._batch_size,
                drop_last=True,
                places=self._place)
        elif self._batch_generator is not None:
            self._data_loader.set_batch_generator(
                self._batch_generator, places=self._place)

    def _optimize_fp32_model(self):
        '''
        Fuse the `conv2d/depthwise_conv2d + bn` in FP32 model.
        '''
        _logger.info("Optimize FP32 model ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)
        graph = _remove_ctrl_vars(graph)
        graph = _apply_pass(self._scope, graph, 'conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'depthwise_conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_transpose_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_eltwiseadd_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph,
                            'depthwise_conv_eltwiseadd_bn_fuse_pass')

        self._program = graph.to_program()

    def _collect_target_varnames(self):
        '''
        Collect the variable names for sampling, and set activation
        variables to be persistable.
        '''
        # TODO(juncaipeng), consider the name_scope of skip_quant
        _logger.info("Collect quantized variable names ...")
        self._quantized_op_pairs = {}

        def collect_var_name(var_name_list, persistable_var_names, op_type):
            for var_name in var_name_list:
                if var_name in persistable_var_names:
                    self._quantized_weight_var_name.add(var_name)
                    self._weight_op_pairs[var_name] = op_type
                else:
                    self._quantized_act_var_name.add(var_name)

        persistable_var_names = _all_persistable_var_names(self._program)
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                op_type = op.type
                if self._is_full_quantize and \
                    op_type not in self._quantizable_op_type:
                    _logger.warning(op_type +
                                    " is not supported for quantization.")
                # For quantized ops, sample inputs and outputs
                if op_type in self._quantizable_op_type:
                    collect_var_name(
                        utils._get_op_input_var_names(op),
                        persistable_var_names, op_type)
                    collect_var_name(
                        utils._get_op_output_var_names(op),
                        persistable_var_names, op_type)
                    # collect quanted op output var name
                    for out_var_name in utils._get_op_output_var_names(op):
                        for in_var_name in utils._get_op_input_var_names(op):
                            if in_var_name in persistable_var_names:
                                self._quantized_op_pairs[
                                    in_var_name] = out_var_name
                # For other op, only sample output scale
                elif op_type in self._out_scale_op_list:
                    collect_var_name(
                        utils._get_op_output_var_names(op),
                        persistable_var_names, op_type)

    def _set_activation_persistable(self):
        '''
        Set activation variables to be persistable, so can obtain 
        the tensor data in sample_data
        '''
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = True

    def _reset_activation_persistable(self):
        '''
        Reset activations to be not persistable.
        '''
        to_erase = []
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = False
                to_erase.append(var.name)
        self._scope.erase(to_erase)

    def _sampling(self):
        '''
        Sample the min/max, abs_max or histogram in every iterations.
        '''
        if self._algo == "abs_max":
            self._sample_abs_max()
        elif self._algo == "avg":
            self._sample_avg()
        elif self._algo == "min_max":
            self._sample_min_max()
        elif self._algo == "mse":
            self._sample_mse()
        elif self._algo == "emd":
            self._sample_emd()
        elif self._algo in ["KL", "hist"]:
            self._sample_histogram()

    def _sample_mse(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("MSE searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2**(self._activation_bits - 1) - 1
                quant_dequant_var = np.round(
                    np.clip(var_tensor, 0.0, scale) / scale *
                    bins) / bins * scale
                mse_loss = ((var_tensor - quant_dequant_var)**2).mean()
                if mse_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = mse_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_emd(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("EMD searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2**(self._activation_bits - 1) - 1
                quant_dequant_var = np.round(
                    np.clip(var_tensor, 0.0, scale) / scale *
                    bins) / bins * scale
                emd_loss = np.abs(
                    np.mean(var_tensor) - np.mean(quant_dequant_var)) + np.abs(
                        np.std(var_tensor) - np.std(quant_dequant_var))
                if emd_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = emd_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_avg(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if (var_name not in self._quantized_var_avg):
                self._quantized_var_avg[var_name] = []
            abs_avg_value = float(np.mean(np.max(  \
            np.abs(var_tensor.reshape(var_tensor.shape[0], -1)), axis=(1))))
            self._quantized_var_avg[var_name].append(abs_avg_value)
            continue

    def _sample_abs_max(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if (var_name not in self._quantized_threshold) or \
                (abs_max_value > self._quantized_threshold[var_name]):
                self._quantized_threshold[var_name] = abs_max_value

    def _sample_min_max(self):
        if self._quantized_var_min == {} and self._quantized_var_max == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    min_value = float(np.min(var_tensor))
                    max_value = float(np.max(var_tensor))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    min_value = []
                    max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            min_value.append(float(np.min(var_tensor[:, i])))
                            max_value.append(float(np.max(var_tensor[:, i])))
                    else:
                        for i in range(var_tensor.shape[0]):
                            min_value.append(float(np.min(var_tensor[i])))
                            max_value.append(float(np.max(var_tensor[i])))
                self._quantized_var_min[var_name] = min_value
                self._quantized_var_max[var_name] = max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if (var_name not in self._quantized_var_min) or \
                (min_value < self._quantized_var_min[var_name]):
                self._quantized_var_min[var_name] = min_value
            if (var_name not in self._quantized_var_max) or \
                (max_value > self._quantized_var_max[var_name]):
                self._quantized_var_max[var_name] = max_value

    def _sample_histogram(self):
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor_abs = np.abs(var_tensor)
            bins = self._sampling_act_histogram[var_name][1]
            hist, _ = np.histogram(var_tensor_abs, bins=bins)
            self._sampling_act_histogram[var_name][0] += hist

    def _save_input_threhold(self):
        '''
        Save input threshold to the quantized op.
        '''
        assert self._algo == "min_max", \
            "The algo should be min_max to save input threshold."
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in self._quantizable_op_type:
                    for var_name in utils._get_op_input_var_names(op):
                        assert var_name in self._quantized_var_min
                        assert var_name in self._quantized_var_max
                        op._set_attr(var_name + ".min",
                                     self._quantized_var_min[var_name])
                        op._set_attr(var_name + ".max",
                                     self._quantized_var_max[var_name])
                        op._set_attr("with_quant_attr", True)

    def _collect_activation_abs_min_max(self):
        '''
        Collect the abs_min and abs_max for all activation. When algo = KL,
        get the min and max value, and then calculate the threshold.
        '''
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = np.abs(var_tensor)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if var_name not in self._sampling_act_abs_min_max:
                self._sampling_act_abs_min_max[
                    var_name] = [min_value, max_value]
            else:
                if min_value < self._sampling_act_abs_min_max[var_name][0]:
                    self._sampling_act_abs_min_max[var_name][0] = min_value
                if max_value > self._sampling_act_abs_min_max[var_name][1]:
                    self._sampling_act_abs_min_max[var_name][1] = max_value

    def _init_sampling_act_histogram(self):
        '''
        Based on the min/max value, init the sampling_act_histogram.
        '''
        for var_name in self._quantized_act_var_name:
            if var_name not in self._sampling_act_histogram:
                min_val = self._sampling_act_abs_min_max[var_name][0]
                max_val = self._sampling_act_abs_min_max[var_name][1]
                hist, hist_edeges = np.histogram(
                    [], bins=self._histogram_bins, range=(min_val, max_val))
                self._sampling_act_histogram[var_name] = [hist, hist_edeges]

    def _calculate_kl_hist_threshold(self):
        '''
        Calculate the KL or hist threshold of quantized variables.
        '''
        _logger.info("Calculate {} threshold ...".format(self._algo))
        assert self._algo in ["KL", "hist"], "The algo should be KL or hist."

        # Abs_max threshold for weights
        for var_name in self._quantized_weight_var_name:
            weight_data = utils.load_variable_data(self._scope, var_name)
            if self._weight_quantize_type == "abs_max":
                weight_threshold = float(np.max(np.abs(weight_data)))
            elif self._weight_quantize_type == "channel_wise_abs_max":
                weight_threshold = []
                if self._weight_op_pairs[
                        var_name] in utils._channelwise_quant_axis1_ops:
                    for i in range(weight_data.shape[1]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[:, i]))))
                else:
                    for i in range(weight_data.shape[0]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[i]))))
            self._quantized_var_threshold[var_name] = weight_threshold

        for var_name in self._quantized_act_var_name:
            hist, hist_edeges = self._sampling_act_histogram[var_name]
            if self._algo == "KL":
                bin_width = hist_edeges[1] - hist_edeges[0]
                self._quantized_var_threshold[var_name] = \
                    cal_kl_threshold(hist, bin_width, self._activation_bits)
            elif self._algo == "hist":
                self._quantized_var_threshold[var_name] = \
                    self._get_hist_scaling_factor(hist, hist_edeges)

    def _update_program(self):
        '''
        Use QuantizationTransformPass and AddQuantDequantPass to insert 
        fake_quantize, fake_dequantize and fake_quant_dequant op. 
        Besides, save all threshold to the scale var node.
        '''
        _logger.info("Update the program ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)

        # use QuantizationTransformPass to insert fake_quant/fake_dequantize op
        major_quantizable_op_types = []
        for op_type in utils._weight_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                major_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            transform_pass = QuantizationTransformPass(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)
        else:
            transform_pass = QuantizationTransformPassV2(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)

        for sub_graph in graph.all_sub_graphs():
            # Insert fake_quant/fake_dequantize op must in test graph, so
            # set per graph's _for_test is True.
            sub_graph._for_test = True
            transform_pass.apply(sub_graph)

        # use AddQuantDequantPass to insert fake_quant_dequant op
        minor_quantizable_op_types = []
        for op_type in utils._act_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                minor_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            add_quant_dequant_pass = AddQuantDequantPass(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types)
        else:
            add_quant_dequant_pass = AddQuantDequantPassV2(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types,
                is_full_quantized=self._is_full_quantize)

        for sub_graph in graph.all_sub_graphs():
            sub_graph._for_test = True
            add_quant_dequant_pass.apply(sub_graph)

        # save threshold to scale var node
        if self._algo in ["KL", "hist"]:
            scale_dict = self._quantized_var_threshold
        else:
            scale_dict = self._quantized_threshold
        for key, val in scale_dict.items():
            utils.set_variable_data(
                self._scope,
                self._place,
                key + ".scale",
                np.array(
                    [val], dtype=np.float32))
            utils.set_variable_data(
                self._scope,
                self._place,
                key + ".quant_dequant.scale",
                np.array(
                    [val], dtype=np.float32))

        if not self._onnx_format:
            # apply QuantizationFreezePass, and obtain the final quant model
            freeze_pass = QuantizationFreezePass(
                scope=self._scope,
                place=self._place,
                bias_correction=self._bias_correction,
                weight_bits=self._weight_bits,
                round_type=self._round_type,
                activation_bits=self._activation_bits,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)

            for sub_graph in graph.all_sub_graphs():
                sub_graph._for_test = True
                freeze_pass.apply(sub_graph)
        else:
            quant_weight_pass = QuantWeightPass(self._scope, self._place)
            for sub_graph in graph.all_sub_graphs():
                sub_graph._for_test = True
                quant_weight_pass.apply(sub_graph)

        self._program = graph.to_program()

    def _save_output_threshold(self):
        '''
        Save output threshold to the quantized op.
        '''

        def save_info(op_node, out_var_name, threshold_map, out_info_name,
                      quantized_type):
            assert out_var_name in threshold_map, \
                "The output ({}) of {} node does not have threshold.".format(
                out_var_name, op_node.type)
            op_node._set_attr(out_info_name, threshold_map[var_name])
            op_node._set_attr("with_quant_attr", True)
            if op_node.type in self._quantizable_op_type:
                op._set_attr("quantization_type", quantized_type)

        def analysis_and_save_info(op_node, out_var_name):
            argname_index = utils._get_output_name_index(op_node, out_var_name)
            assert argname_index is not None, \
                out_var_name + " is not the output of the op"
            if self._algo == "KL":
                # For compatibility, we save output threshold by two methods.
                save_info(op_node, out_var_name, self._quantized_var_threshold,
                          "out_threshold", "post_kl")
                save_info(
                    op_node, out_var_name, self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_kl")
            elif self._algo == "hist":
                # For compatibility, we save output threshold by two methods.
                save_info(op_node, out_var_name, self._quantized_var_threshold,
                          "out_threshold", "post_hist")
                save_info(
                    op_node, out_var_name, self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_hist")

            elif self._algo in ["avg", "abs_max", "mse", "emd"]:
                save_info(op_node, out_var_name, self._quantized_threshold,
                          "out_threshold", "post_" + str(self._algo))
                save_info(
                    op_node, out_var_name, self._quantized_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_" + str(self._algo))
            elif self._algo == "min_max":
                save_info(op_node, out_var_name, self._quantized_var_min,
                          "out_min", "post_min_max")
                save_info(op_node, out_var_name, self._quantized_var_max,
                          "out_max", "post_min_max")

        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in (
                        self._quantizable_op_type + self._out_scale_op_list):
                    out_var_names = utils._get_op_output_var_names(op)
                    for var_name in out_var_names:
                        analysis_and_save_info(op, var_name)

    def _collect_dynamic_quantize_op_threshold(self, target_ops_type):
        """
        Collect and save the weight threshold for dynamic quantize ops,
        such as lstm and gru.
        Args:
            target_ops_type(list): the op type of target ops
        Returns:
            None
        """

        target_ops = []
        for index in range(self._program.num_blocks):
            for op in self._program.block(index).ops:
                if op.type in target_ops_type:
                    target_ops.append(op)

        quantization_type = str("post_" + self._algo).lower()
        persistable_var_names = _all_persistable_var_names(self._program)
        for op in target_ops:
            for var_name in utils._get_op_input_var_names(op):
                if var_name in persistable_var_names:
                    var_data = utils.load_variable_data(self._scope, var_name)
                    threshold = float(np.max(np.abs(var_data)))
                    argname, index = utils._get_input_name_index(op, var_name)
                    op._set_attr(argname + str(index) + "_threshold", threshold)
                    op._set_attr("quantization_type", quantization_type)
                    op._set_attr("bit_length", self._weight_bits)
                    op._set_attr("with_quant_attr", True)

    def _get_hist_scaling_factor(self, hist, hist_edges):
        '''
        Using the hist method to get the scaling factor.
        '''
        threshold_rate = self._hist_percent
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edges[1] - hist_edges[0]
        return (hist_index - 0.5) * bin_width


class WeightQuantization(object):
    _supported_quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
    _supported_weight_quantize_type = ['channel_wise_abs_max', 'abs_max']

    def __init__(self, model_dir, model_filename=None, params_filename=None):
        '''
        This class quantizes the weight of some ops to reduce the size of model
        or improve the perforemace.

        Args:
            model_dir(str): The path of the fp32 model that will be quantized,
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference
                program. If it is None, the default filename '__model__' will
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it
                as the real filename. If parameters were saved in separate files,
                set it as 'None'. Default is 'None'.
        '''
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename

    def quantize_weight_to_int(self,
                               save_model_dir,
                               save_model_filename=None,
                               save_params_filename=None,
                               quantizable_op_type=["conv2d", "mul"],
                               weight_bits=8,
                               weight_quantize_type="channel_wise_abs_max",
                               generate_test_model=False,
                               threshold_rate=0.0):
        '''
        In order to reduce the size of model, this api quantizes the weight
        of some ops from float32 to int8/16. In the inference stage, the 
        quantized weight will be dequantized to float32 again.
        
        Args:
            save_model_dir(str): The path to save the quantized model.
            save_model_filename(str, optional): The name of file to 
                save the inference program. If it is None, the default 
                filename '__model__' will be used. Default is 'None'.
            save_params_filename(str, optional): The name of file to 
                save all parameters. If it is None, parameters were 
                saved in separate files. If it is not None, all 
                parameters were saved in a single binary file.
            quantizable_op_type(list[str], optional): The list of ops 
                that will be quantized, and the quantized ops should be
                contained in ["conv2d", "depthwise_conv2d", "mul"]. 
                Default is ["conv2d","mul"].
            weight_bits(int, optional): The bits for the quantized weight, 
                and it should be 8 or 16. Default is 8.
            weight_quantize_type(str, optional): quantization type for weights,
                support 'channel_wise_abs_max' and 'abs_max'. Set it as
                'channel_wise_abs_max', the accuracy performs better.
            generate_test_model(bool, optional): If set generate_test_model 
                as True, it saves a fake quantized model, in which the weights 
                are quantized and dequantized. We can use PaddlePaddle to load 
                the fake quantized model and test the accuracy on GPU or CPU.
            threshold_rate(float, optional): This api uses abs_max methd to 
                quantize the weight from float32 to int8/16, and the abs max 
                value is important for quantization diff. When the abs_max 
                value is far away from the center of the numerical distribution, 
                we can set threshold_rate between 1e-6 and 1e-8, so the abs max 
                value will be optimized. Default is 0.0.
        '''
        for op_type in quantizable_op_type:
            assert op_type in self._supported_quantizable_op_type, \
                "Input error:" + op_type + \
                " is not supported for weight quantization."
        assert weight_bits in [8, 16], \
            "Input error: weight_bits should be 8 or 16."
        assert weight_quantize_type in self._supported_weight_quantize_type, \
            "Input error: weight_quantize_type should in {}".format(
                self._supported_weight_quantize_type)

        quantized_model_dir = os.path.join(save_model_dir, "quantized_model")
        self._quantize_weight_to_int(quantized_model_dir, save_model_filename,
                                     save_params_filename, quantizable_op_type,
                                     weight_bits, weight_quantize_type, False,
                                     threshold_rate)

        if generate_test_model:
            test_model_dir = os.path.join(save_model_dir, "test_model")
            self._quantize_weight_to_int(
                test_model_dir, save_model_filename, save_params_filename,
                quantizable_op_type, weight_bits, weight_quantize_type, True,
                threshold_rate)

    def convert_weight_to_fp16(self, save_model_dir):
        """
        Convert all presistable vars from fp32 to fp16.
        Note that, this api only changes the data type of variables in
        __params__ file, and the __model__ file remains unchanged. 

        Args:
            save_model_dir(str): The path to save the fp16 model.
        """

        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [infer_program, feed_list, fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=exe,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        # Clone and save fp16 weights
        save_program = framework.Program()
        save_block = save_program.global_block()
        save_var_map = {}

        for var in infer_program.list_vars():
            if (var.type == core.VarDesc.VarType.RAW) or \
                (not var.persistable) or (var.name in ['feed', 'fetch']) \
                or (var.dtype != core.VarDesc.VarType.FP32):
                continue

            #new_var = _clone_var_to_block_(var, save_block)
            new_var = save_block._clone_variable(var)
            if self._params_filename is not None:
                save_var_map[new_var.name] = new_var
            else:
                save_file_path = os.path.join(
                    os.path.normpath(save_model_dir), new_var.name)
                save_block.append_op(
                    type='save',
                    inputs={'X': [new_var]},
                    outputs={},
                    attrs={
                        'file_path': os.path.normpath(save_file_path),
                        'save_as_fp16': True
                    })

        if self._params_filename is not None:
            save_var_list = []
            for name in sorted(save_var_map.keys()):
                save_var_list.append(save_var_map[name])

            saved_params_var = save_block.create_var(
                type=core.VarDesc.VarType.RAW,
                name=unique_name.generate("saved_params"))
            saved_params_var.desc.set_persistable(True)

            save_path = os.path.join(
                os.path.normpath(save_model_dir), self._params_filename)
            save_block.append_op(
                type='save_combine',
                inputs={'X': save_var_list},
                outputs={'Y': saved_params_var},
                attrs={'file_path': save_path,
                       'save_as_fp16': True})

        save_program._sync_with_cpp()
        exe.run(save_program)

        # Copy model
        model_filename = "__model__" if self._model_filename is None \
                    else self._model_filename
        src_model = os.path.join(self._model_dir, model_filename)
        dest_model = os.path.join(save_model_dir, model_filename)
        shutil.copyfile(src_model, dest_model)

    def _quantize_weight_to_int(self, save_model_dir, save_model_filename,
                                save_params_filename, quantizable_op_type,
                                weight_bits, weight_quantize_type, for_test,
                                threshold_rate):
        """
        Generate quantized model or fake quantized model.
        """
        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [program, feed_list, fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=exe,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        quantized_ops = []
        for index in range(program.num_blocks):
            block = program.block(index)
            for op in block.ops:
                if op.type in quantizable_op_type:
                    quantized_ops.append(op)

        # Quantize weights
        persistable_var_names = _all_persistable_var_names(program)
        for op in quantized_ops:
            for var_name in op.input_arg_names:
                if var_name in persistable_var_names:
                    if weight_quantize_type == "abs_max":
                        self._weight_abs_max_quantization(
                            scope, place, weight_bits, threshold_rate, op,
                            var_name, for_test)
                    elif weight_quantize_type == "channel_wise_abs_max":
                        self._weight_channel_wise_abs_max_quantization(
                            scope, place, weight_bits, op, var_name, for_test)

        io.save_inference_model(
            dirname=save_model_dir,
            feeded_var_names=feed_list,
            target_vars=fetch_list,
            executor=exe,
            main_program=program,
            model_filename=save_model_filename,
            params_filename=save_params_filename)

    def _weight_abs_max_quantization(self, scope, place, weight_bits,
                                     threshold_rate, op, var_name, for_test):
        '''
        Use abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if abs(threshold_rate) < 1e-10:
            threshold_value = np.max(np.abs(weight_data))
        else:
            threshold_value = self._calculate_threshold(\
                weight_data, threshold_rate)
            weight_data[weight_data > threshold_value] = threshold_value
            weight_data[weight_data < -threshold_value] = -threshold_value
        scale = threshold_value / quantize_range
        quantized_weight_data = \
            np.around(weight_data / scale).astype(save_weight_dtype)

        # Set weight data
        if not for_test:
            utils.set_variable_data(scope, place, var_name,
                                    quantized_weight_data)
        else:
            dequantized_weight_data = \
                (quantized_weight_data * scale).astype(np.float32)
            utils.set_variable_data(scope, place, var_name,
                                    dequantized_weight_data)

        # Save info
        op._set_attr('quantization_type', 'post_weight_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", [scale])  # Save as list
        op._set_attr("with_quant_attr", True)

    def _weight_channel_wise_abs_max_quantization(
            self, scope, place, weight_bits, op, var_name, for_test):
        ''' 
        Use channel_wise_abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if op.type == "mul":
            scales, quantized_weight_data = \
                self._mul_channel_wise_quantization(weight_data,
                    quantize_range, save_weight_dtype)
        elif op.type in ["conv2d", "depthwise_conv2d"]:
            scales, quantized_weight_data = \
                self._conv_channel_wise_quantization(weight_data,
                    quantize_range, save_weight_dtype)
        else:
            _logger.error(op.type + " is not supported by weight quantization")

        # Set weight data
        if not for_test:
            utils.set_variable_data(scope, place, var_name,
                                    quantized_weight_data)
        else:
            if op.type == "mul":
                dequantized_weight_data = \
                    self._mul_channel_wise_dequantization(quantized_weight_data, scales)
            elif op.type in ["conv2d", "depthwise_conv2d"]:
                dequantized_weight_data = \
                    self._conv_channel_wise_dequantization(quantized_weight_data, scales)
            else:
                _logger.error(op.type +
                              " is not supported by weight quantization")
            utils.set_variable_data(scope, place, var_name,
                                    dequantized_weight_data)

        # Save info
        op._set_attr('quantization_type', 'post_weight_channel_wise_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", scales)
        op._set_attr("with_quant_attr", True)

    def _conv_channel_wise_quantization(self, weight_data, quantize_range,
                                        save_weight_dtype):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(
            weight_data, dtype=save_weight_dtype)
        channel_num = weight_data.shape[0]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[i] = \
                np.around(weight_data[i] / scale).astype(save_weight_dtype)
        return scales, quantized_weight_data

    def _conv_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For conv2d and depthwise_conv2d, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(
            quantized_weight_data, dtype=np.float32)
        for i in range(len(scales)):
            dequantized_weight_data[i] = \
                (quantized_weight_data[i] * scales[i]).astype(np.float32)
        return dequantized_weight_data

    def _mul_channel_wise_quantization(self, weight_data, quantize_range,
                                       save_weight_dtype):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(
            weight_data, dtype=save_weight_dtype)
        channel_num = weight_data.shape[-1]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[:, i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[:, i] = \
                np.around(weight_data[:, i] / scale).astype(save_weight_dtype)
        return scales, quantized_weight_data

    def _mul_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For mul, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(
            quantized_weight_data, dtype=np.float32)
        for i in range(len(scales)):
            dequantized_weight_data[:, i] = \
                (quantized_weight_data[:, i] * scales[i]).astype(np.float32)
        return dequantized_weight_data

    def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
        input_abs = np.abs(input)
        hist, hist_edeges = np.histogram(
            input_abs, bins=histogram_bins, range=(0, np.max(input_abs)))
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= 1.0 - threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edeges[1] - hist_edeges[0]
        return hist_index * bin_width